From 14b8f85ee5bb59ad2ea09760ad749ca59695bc37 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 7 Nov 2024 13:02:38 +0000
Subject: [PATCH 01/41] Color wires changes. wires are coloured used by default
 when the diagram has frames but can be turned off using flag
 `color_wires=False`

---
 lambeq/backend/drawing/drawable.py        | 92 ++++++++++++++++++++---
 lambeq/backend/drawing/drawing.py         | 64 ++++++++++++++--
 lambeq/backend/drawing/drawing_backend.py | 28 ++++++-
 lambeq/backend/drawing/mat_backend.py     | 21 +++++-
 4 files changed, 184 insertions(+), 21 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 3abe57c7..4c3d0bc4 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -95,7 +95,7 @@ class WireEndpoint:
 
     x: float
     y: float
-
+    color: int = 0 # New attribute for wire noun
     parent: Optional['BoxNode'] = None
 
     @property
@@ -345,7 +345,8 @@ def _add_box(self,
                  box: grammar.Box,
                  off: int,
                  x_pos: float,
-                 y_pos: float) -> tuple[list[int], int]:
+                 y_pos: float,
+                 input_nouns: list[str]=None) -> tuple[list[int], int, list[str]]:
         """Add a box to the graph, creating necessary wire endpoints.
 
         Returns
@@ -354,25 +355,63 @@ def _add_box(self,
             The new scan of wire endpoints after adding the box
         box_ind : int
             The index of the newly added `BoxNode`
+        list[int]
+            The new order of input_nouns after adding the box
         """
         node = BoxNode(box, x_pos, y_pos)
 
         box_ind = self._add_boxnode(node)
+        num_input = len(box.dom)
+        for i in range(num_input):
+            if i < len(input_nouns):
+                pass
+            else:
+                # If we run out of input nouns, generate new ones
+                new_color = self.get_noun_id()
+                input_nouns.append(new_color)
 
         # Create a node representing each element in the box's domain
         for i, obj in enumerate(box.dom):
+            idx = off + i
             nbr_idx = scan[off + i]
+            noun_id = input_nouns[idx] if input_nouns and idx < len(
+                input_nouns) else DrawableDiagramWithFrames.get_noun_id() # Default to black if no input color available
+
             wire_end = WireEndpoint(WireEndpointType.DOM,
                                     obj=obj,
                                     x=self.wire_endpoints[nbr_idx].x,
-                                    y=y_pos + HALF_BOX_HEIGHT)
+                                    y=y_pos + HALF_BOX_HEIGHT,
+                                    color=noun_id)
 
             wire_idx = self._add_wire_end(wire_end)
             node.add_dom_wire(wire_idx)
             self._add_wire(nbr_idx, wire_idx)
 
         scan_insert = []
-
+        # if Swap, exchange the noun_ids
+        if isinstance(box, grammar.Swap):
+            if input_nouns and len(box.dom) > 1:
+                dom_idx_1 = off
+                dom_idx_2 = off + 1
+                input_nouns[dom_idx_1], input_nouns[dom_idx_2] = input_nouns[dom_idx_2], input_nouns[dom_idx_1]
+        # if Spider, expand or shrink the noun_ids based on type
+        elif isinstance(node.obj, grammar.Spider):
+            if len(box.dom) == 1 and len(box.cod) > 1:
+                dom_noun = input_nouns[off] if input_nouns and off < len(input_nouns) else DrawableDiagramWithFrames.get_noun_id()
+                expanded_colors = [dom_noun] * len(box.cod)
+                input_nouns = input_nouns[:off] + expanded_colors + input_nouns[off + len(box.dom):]
+            elif len(box.dom) > 1 and len(box.cod) == 1:
+                cod_noun = input_nouns[off] if input_nouns and off < len(input_nouns) else DrawableDiagramWithFrames.get_noun_id()
+                input_nouns = input_nouns[:off] + [cod_noun] + input_nouns[off + len(box.dom):]
+
+        num_output = off+len(box.cod)
+        for i in range(num_output):
+            if i < len(input_nouns):
+                pass
+            else:
+                # If we run out of input nouns, generate new ones
+                new_color = self.get_noun_id()
+                input_nouns.append(new_color)
         # Create a node representing each element in the box's codomain
         for i, obj in enumerate(box.cod):
 
@@ -383,18 +422,20 @@ def _add_box(self,
             else:
                 x = x_pos + X_SPACING * (i - len(box.cod[1:]) / 2)
             y = y_pos - HALF_BOX_HEIGHT
-
+            idx = off + i
+            noun_id = input_nouns[idx] if input_nouns and idx < len(input_nouns) else DrawableDiagramWithFrames.get_noun_id()
             wire_end = WireEndpoint(WireEndpointType.COD,
                                     obj=obj,
                                     x=x,
-                                    y=y)
+                                    y=y,
+                                    color=noun_id)
 
             wire_idx = self._add_wire_end(wire_end)
             scan_insert.append(wire_idx)
             node.add_cod_wire(wire_idx)
 
         # Replace node's dom with its cod in scan
-        return scan[:off] + scan_insert + scan[off + len(box.dom):], box_ind
+        return scan[:off] + scan_insert + scan[off + len(box.dom):], box_ind, input_nouns
 
     def _find_box_edges(self,
                         box: grammar.Box,
@@ -778,7 +819,8 @@ class DrawableDiagramWithFrames(DrawableDiagram):
     frame, carrying all information necessary to render it.
 
     """
-
+    #add counter for Nouns
+    noun_id_counter = 0
     def _make_space(self,
                     scan: list[int],
                     box: grammar.Box,
@@ -949,6 +991,15 @@ def calculate_bounds(self) -> tuple[float, float, float, float]:
 
         return min(all_xs), min(all_ys), max(all_xs), max(all_ys)
 
+
+    @staticmethod
+    def get_noun_id() -> int:
+        """Generate a new numerical ID for the noun box/wire."""
+        # Increment and return the next available ID
+        noun_id = DrawableDiagramWithFrames.noun_id_counter
+        DrawableDiagramWithFrames.noun_id_counter += 1
+        return noun_id
+
     @classmethod
     def from_diagram(cls,
                      diagram: grammar.Diagram,
@@ -976,12 +1027,19 @@ def from_diagram(cls,
         drawable = cls()
 
         scan = []
+        # Generate unique noun_ids for input wires
+        num_input = len(diagram.dom)
+        input_nouns = []
+        for i in range(num_input):
+                new_color = drawable.get_noun_id()
+                input_nouns.append(new_color)
 
         for i, obj in enumerate(diagram.dom):
             wire_end = WireEndpoint(WireEndpointType.INPUT,
                                     obj=obj,
                                     x=X_SPACING * i,
-                                    y=1)
+                                    y=1,
+                                    color=input_nouns[i])
             wire_end_idx = drawable._add_wire_end(wire_end)
             scan.append(wire_end_idx)
 
@@ -993,7 +1051,7 @@ def from_diagram(cls,
             # TODO: Debug issues with y coord
             x, y = drawable._make_space(scan, box, off, foliated=foliated)
 
-            scan, box_ind = drawable._add_box(scan, box, off, x, y)
+            scan, box_ind, input_nouns = drawable._add_box(scan, box, off, x, y, input_nouns)
             box_height = BOX_HEIGHT
             # Add drawables for the inside of the frame
             if isinstance(box, grammar.Frame):
@@ -1004,12 +1062,24 @@ def from_diagram(cls,
             max_box_half_height = max(max_box_half_height, (box_height / 2))
             min_y = min(min_y, y)
 
+        num_output = len(diagram.cod)
+        output_nouns = []
+        # Match output nouns with input nouns as much as possible
+        for i in range(num_output):
+            if i < len(input_nouns):
+                pass
+            else:
+                # If we run out of input nouns, generate new ones
+                new_color = drawable.get_noun_id()
+                input_nouns.append(new_color)
+
         for i, obj in enumerate(diagram.cod):
             wire_end = WireEndpoint(
                 WireEndpointType.OUTPUT,
                 obj=obj,
                 x=drawable.wire_endpoints[scan[i]].x,
-                y=min_y - max_box_half_height - 1.5 * BOX_HEIGHT
+                y=min_y - max_box_half_height - 1.5 * BOX_HEIGHT,
+                color=input_nouns[i]
             )
             wire_end_idx = drawable._add_wire_end(wire_end)
             drawable._add_wire(scan[i], wire_end_idx)
diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index bf85b75b..222e44b3 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -40,7 +40,8 @@
                                                     DEFAULT_ASPECT,
                                                     DEFAULT_MARGINS,
                                                     DrawingBackend,
-                                                    FRAME_COLORS)
+                                                    FRAME_COLORS,
+                                                    WIRE_COLORS)
 from lambeq.backend.drawing.helpers import drawn_as_spider, needs_asymmetry
 from lambeq.backend.drawing.mat_backend import MatBackend
 from lambeq.backend.drawing.text_printer import PregroupTextPrinter
@@ -110,6 +111,9 @@ def draw(diagram: Diagram, **params) -> None:
     params['coloring_mode'] = params.get(
         'coloring_mode', ColoringMode.TYPE,
     )
+    params['color_wires'] = params.get(
+        'color_wires', diagram.has_frames,
+    )
     if drawable is None:
         drawable = drawable_cls.from_diagram(diagram,
                                              params.get('foliated', False))
@@ -144,10 +148,11 @@ def draw(diagram: Diagram, **params) -> None:
             backend = _draw_controlled_gate(backend, drawable, node, **params)
         elif not drawn_as_spider(node.obj):
             backend = _draw_box(backend, drawable, node, **params)
-
+        else:
+            wire_drawings = compute_spider_wires(node, drawable, **params)
+            backend.draw_spider(node,wire_drawings,**params)
     # Draw boxes first since they are filled
     backend = _draw_wires(backend, drawable, **params)
-    backend.draw_spiders(drawable, **params)
     backend.output(
         path=params.get('path', None),
         baseline=0,
@@ -155,6 +160,44 @@ def draw(diagram: Diagram, **params) -> None:
         show=params.get('show', True),
         margins=params.get('margins', DEFAULT_MARGINS))
 
+def compute_spider_wires(node, drawable, **params)-> list:
+        """
+        Compute the wire drawing requirements for spider.
+
+         Computes all the wires (dom and cod) that need to be drawn for spider
+        and returns a list of wires.
+        """
+        wire_color = 'black'
+        wire_drawings = []
+        # Compute wires for cod_wires (outgoing wires)
+        for wire in node.cod_wires:
+            start_coordinates = node.coordinates
+            end_coordinates = drawable.wire_endpoints[wire].coordinates
+            if params['color_wires']:
+                wire_color = WIRE_COLORS[(drawable.wire_endpoints[wire].color - 1) % len(WIRE_COLORS)]
+            wire_drawings.append({
+                'start': start_coordinates,
+                'end': end_coordinates,
+                'bend_out': True,
+                'is_leg': True,
+                'color': wire_color
+            })
+
+        # Compute wires for dom_wires (incoming wires)
+        for wire in node.dom_wires:
+            start_coordinates = drawable.wire_endpoints[wire].coordinates
+            end_coordinates = node.coordinates
+            if params['color_wires']:
+                wire_color = WIRE_COLORS[(drawable.wire_endpoints[wire].color - 1) % len(WIRE_COLORS)]
+            wire_drawings.append({
+                'start': start_coordinates,
+                'end': end_coordinates,
+                'bend_in': True,
+                'is_leg': True,
+                'color': wire_color
+            })
+
+        return wire_drawings
 
 def draw_pregroup(diagram: Diagram, **params) -> None:
     """ Draw a pregroup grammar diagram.
@@ -458,7 +501,9 @@ def _get_box_color(box: grammar.Diagrammable,
             color = FRAME_COLORS[(frame_attr - 1) % len(FRAME_COLORS)]
 
     return color
-
+def _get_wire_color(wire_id):
+    wire_color = WIRE_COLORS[(wire_id - 1) % len(WIRE_COLORS)]
+    return wire_color
 
 def _draw_pregroup_state(backend: DrawingBackend,
                          drawable_box: BoxNode,
@@ -524,9 +569,16 @@ def _draw_wires(backend: DrawingBackend,
     for src_idx, tgt_idx in drawable_diagram.wires:
         source = drawable_diagram.wire_endpoints[src_idx]
         target = drawable_diagram.wire_endpoints[tgt_idx]
-
+        wire_color = 'black'
+        if params['color_wires']:
+            # Determine the color based on the type of the source
+            if source.kind in {WireEndpointType.INPUT}:
+                wire_color_id = source.color
+            else:
+                wire_color_id = target.color
+            wire_color = _get_wire_color(wire_color_id)
         backend.draw_wire(
-            source.coordinates, target.coordinates)
+            source.coordinates, target.coordinates, color=wire_color)
 
         if (params.get('draw_type_labels', True) and source.kind in
                 {WireEndpointType.INPUT, WireEndpointType.COD}):
diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index c99b24f3..e49a2e3c 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -30,6 +30,15 @@
 DEFAULT_MARGINS = (.05, .1)
 DEFAULT_ASPECT = 'equal'
 
+WIRE_COLORS = [
+    "#A22417", "#D17800", "#F4A940", "#49A141", "#007765",
+    "#398889", "#0252A1", "#3831A0","#A629B3", "#B60539",
+    "#73190E", "#BE660F", "#F29A3B", "#3C8331", "#01594C",
+    "#2F7173", "#013B73", "#292372", "#271296", "#9526AF",
+    "#960131", "#450D06", "#9C540E", "#F07E33", "#003C33",
+    "#205356", "#002B54", "#201B5B", "#1D0B66", "#751FA5"
+]
+
 
 FRAME_COLORS: list[str] = [
     '#fbe8e7', '#fee1ba', '#fff9e5', '#e8f8ea', '#dcfbf5',
@@ -146,7 +155,8 @@ def draw_wire(self,
                   bend_out: bool = False,
                   bend_in: bool = False,
                   is_leg: bool = False,
-                  style: str | None = None) -> None:
+                  style: str | None = None,
+                  color: str = 'black') -> None:
         """
         Draws a wire from source to target, possibly with a curve
 
@@ -184,6 +194,22 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
 
         """
 
+    @abstractmethod
+    def draw_spider(self, node, wire_drawings, **params) -> None:
+        """
+        Draws the spider node with the list of wires.
+
+        Parameters
+        ----------
+        node: DrawableDiagram
+            the node to be drawn.
+        wire_drawings: list of wires
+            list of wires in the spider
+        params: any
+            Additional parameters.
+
+        """
+
     @abstractmethod
     def output(self,
                path: str | None = None,
diff --git a/lambeq/backend/drawing/mat_backend.py b/lambeq/backend/drawing/mat_backend.py
index 8392d16c..30e901ac 100644
--- a/lambeq/backend/drawing/mat_backend.py
+++ b/lambeq/backend/drawing/mat_backend.py
@@ -71,11 +71,12 @@ def draw_wire(self,
                   bend_out: bool = False,
                   bend_in: bool = False,
                   is_leg: bool = False,
-                  style: str | None = None) -> None:
+                  style: str | None = None,
+                  color: str = 'black') -> None:
         if style == '->':
             self.axis.arrow(
                 *(source + (target[0] - source[0], target[1] - source[1])),
-                head_width=.02, color='black')
+                head_width=.02, color=color)
         else:
             if is_leg:
                 mid = (target[0], source[1])
@@ -107,7 +108,7 @@ def draw_wire(self,
                 ])
 
             self.axis.add_patch(PathPatch(
-                path, facecolor='none', linewidth=self.linewidth))
+                path, facecolor='none', linewidth=self.linewidth, edgecolor=color))
 
         self.max_width = max(self.max_width, source[0], target[0])
 
@@ -128,6 +129,20 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
                                bend_in=True,
                                is_leg=True)
 
+    def draw_spider(self, node, wire_drawings, **params) -> None:
+        if isinstance(node.obj, Spider):
+            self.draw_node(*node.coordinates, **params)
+
+        for wire in wire_drawings:
+            self.draw_wire(
+                wire['start'],
+                wire['end'],
+                bend_out=wire.get('bend_out', False),
+                bend_in=wire.get('bend_in', False),
+                is_leg=wire['is_leg'],
+                color=wire['color']
+            )
+
     def output(self,
                path: str | None = None,
                show: bool = True,

From 0b3f2752dea092212af08b814cc44c12bcfb34f6 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Fri, 8 Nov 2024 10:45:32 +0000
Subject: [PATCH 02/41] review comments fixes, remove colors looks like similar

---
 lambeq/backend/drawing/drawable.py        | 13 ++---
 lambeq/backend/drawing/drawing.py         | 63 +++++------------------
 lambeq/backend/drawing/drawing_backend.py | 34 ++++++------
 lambeq/backend/drawing/mat_backend.py     | 30 ++++-------
 4 files changed, 47 insertions(+), 93 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 4c3d0bc4..e88688d6 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -95,7 +95,7 @@ class WireEndpoint:
 
     x: float
     y: float
-    color: int = 0 # New attribute for wire noun
+    noun_id: int = 0 # New attribute for wire noun
     parent: Optional['BoxNode'] = None
 
     @property
@@ -381,7 +381,7 @@ def _add_box(self,
                                     obj=obj,
                                     x=self.wire_endpoints[nbr_idx].x,
                                     y=y_pos + HALF_BOX_HEIGHT,
-                                    color=noun_id)
+                                    noun_id=noun_id)
 
             wire_idx = self._add_wire_end(wire_end)
             node.add_dom_wire(wire_idx)
@@ -428,7 +428,7 @@ def _add_box(self,
                                     obj=obj,
                                     x=x,
                                     y=y,
-                                    color=noun_id)
+                                    noun_id=noun_id)
 
             wire_idx = self._add_wire_end(wire_end)
             scan_insert.append(wire_idx)
@@ -820,7 +820,7 @@ class DrawableDiagramWithFrames(DrawableDiagram):
 
     """
     #add counter for Nouns
-    noun_id_counter = 0
+    noun_id_counter = 1
     def _make_space(self,
                     scan: list[int],
                     box: grammar.Box,
@@ -1039,7 +1039,7 @@ def from_diagram(cls,
                                     obj=obj,
                                     x=X_SPACING * i,
                                     y=1,
-                                    color=input_nouns[i])
+                                    noun_id=input_nouns[i])
             wire_end_idx = drawable._add_wire_end(wire_end)
             scan.append(wire_end_idx)
 
@@ -1079,7 +1079,7 @@ def from_diagram(cls,
                 obj=obj,
                 x=drawable.wire_endpoints[scan[i]].x,
                 y=min_y - max_box_half_height - 1.5 * BOX_HEIGHT,
-                color=input_nouns[i]
+                noun_id=input_nouns[i]
             )
             wire_end_idx = drawable._add_wire_end(wire_end)
             drawable._add_wire(scan[i], wire_end_idx)
@@ -1454,6 +1454,7 @@ def _merge_with(self, drawable: 'DrawableDiagramWithFrames') -> None:
         last_wire_endpoint = len(self.wire_endpoints)
 
         for wire_endpoint in drawable.wire_endpoints:
+            # wire_endpoint.noun_id = 0
             self.wire_endpoints.append(wire_endpoint)
 
         for box in drawable.boxes:
diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index 783f3dfd..1df272c4 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -148,11 +148,10 @@ def draw(diagram: Diagram, **params) -> None:
             backend = _draw_controlled_gate(backend, drawable, node, **params)
         elif not drawn_as_spider(node.obj):
             backend = _draw_box(backend, drawable, node, **params)
-        else:
-            wire_drawings = compute_spider_wires(node, drawable, **params)
-            backend.draw_spider(node,wire_drawings,**params)
+
     # Draw boxes first since they are filled
     backend = _draw_wires(backend, drawable, **params)
+    backend.draw_spiders(drawable, **params)
     backend.output(
         path=params.get('path', None),
         baseline=0,
@@ -160,44 +159,6 @@ def draw(diagram: Diagram, **params) -> None:
         show=params.get('show', True),
         margins=params.get('margins', DEFAULT_MARGINS))
 
-def compute_spider_wires(node, drawable, **params)-> list:
-        """
-        Compute the wire drawing requirements for spider.
-
-         Computes all the wires (dom and cod) that need to be drawn for spider
-        and returns a list of wires.
-        """
-        wire_color = 'black'
-        wire_drawings = []
-        # Compute wires for cod_wires (outgoing wires)
-        for wire in node.cod_wires:
-            start_coordinates = node.coordinates
-            end_coordinates = drawable.wire_endpoints[wire].coordinates
-            if params['color_wires']:
-                wire_color = WIRE_COLORS[(drawable.wire_endpoints[wire].color - 1) % len(WIRE_COLORS)]
-            wire_drawings.append({
-                'start': start_coordinates,
-                'end': end_coordinates,
-                'bend_out': True,
-                'is_leg': True,
-                'color': wire_color
-            })
-
-        # Compute wires for dom_wires (incoming wires)
-        for wire in node.dom_wires:
-            start_coordinates = drawable.wire_endpoints[wire].coordinates
-            end_coordinates = node.coordinates
-            if params['color_wires']:
-                wire_color = WIRE_COLORS[(drawable.wire_endpoints[wire].color - 1) % len(WIRE_COLORS)]
-            wire_drawings.append({
-                'start': start_coordinates,
-                'end': end_coordinates,
-                'bend_in': True,
-                'is_leg': True,
-                'color': wire_color
-            })
-
-        return wire_drawings
 
 def draw_pregroup(diagram: Diagram, **params) -> None:
     """ Draw a pregroup grammar diagram.
@@ -501,9 +462,13 @@ def _get_box_color(box: grammar.Diagrammable,
             color = FRAME_COLORS[(frame_attr - 1) % len(FRAME_COLORS)]
 
     return color
+
 def _get_wire_color(wire_id):
-    wire_color = WIRE_COLORS[(wire_id - 1) % len(WIRE_COLORS)]
-    return wire_color
+    if wire_id == 0:
+        return '#000000'
+    else:
+        wire_color = WIRE_COLORS[(wire_id - 1) % len(WIRE_COLORS)]
+        return wire_color
 
 def _draw_pregroup_state(backend: DrawingBackend,
                          drawable_box: BoxNode,
@@ -569,16 +534,16 @@ def _draw_wires(backend: DrawingBackend,
     for src_idx, tgt_idx in drawable_diagram.wires:
         source = drawable_diagram.wire_endpoints[src_idx]
         target = drawable_diagram.wire_endpoints[tgt_idx]
-        wire_color = 'black'
-        if params['color_wires']:
+        wire_color_id = 0
+        if params.get('color_wires'):
             # Determine the color based on the type of the source
             if source.kind in {WireEndpointType.INPUT}:
-                wire_color_id = source.color
+                wire_color_id = source.noun_id
             else:
-                wire_color_id = target.color
-            wire_color = _get_wire_color(wire_color_id)
+                wire_color_id = target.noun_id
+        # print('wire', wire_color_id)
         backend.draw_wire(
-            source.coordinates, target.coordinates, color=wire_color)
+            source.coordinates, target.coordinates, color_id=wire_color_id, **params)
 
         if (params.get('draw_type_labels', True) and source.kind in
                 {WireEndpointType.INPUT, WireEndpointType.COD}):
diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index e49a2e3c..b3c3df70 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -31,12 +31,12 @@
 DEFAULT_ASPECT = 'equal'
 
 WIRE_COLORS = [
-    "#A22417", "#D17800", "#F4A940", "#49A141", "#007765",
-    "#398889", "#0252A1", "#3831A0","#A629B3", "#B60539",
-    "#73190E", "#BE660F", "#F29A3B", "#3C8331", "#01594C",
-    "#2F7173", "#013B73", "#292372", "#271296", "#9526AF",
-    "#960131", "#450D06", "#9C540E", "#F07E33", "#003C33",
-    "#205356", "#002B54", "#201B5B", "#1D0B66", "#751FA5"
+    '#9c540e', '#f07e33', '#1d0b66', '#751fa5', '#740127',
+    '#a22417', '#d03b2d', '#fe9100', '#f8c84b', '#5ac358',
+    '#018c76', '#450d06', '#066ee2', '#574cfa', '#7446f2',
+    '#c330b9', '#e2074c', '#d17800', '#f4a940', '#49a141',
+    '#007765', '#398889', '#0252a1', '#3831a0', '#271296',
+    '#a629b3', '#b60539' '#3c8331', '#fea431', '#73b8fd'
 ]
 
 
@@ -156,7 +156,8 @@ def draw_wire(self,
                   bend_in: bool = False,
                   is_leg: bool = False,
                   style: str | None = None,
-                  color: str = 'black') -> None:
+                  color_id: int = 0,
+                  **params)-> None:
         """
         Draws a wire from source to target, possibly with a curve
 
@@ -194,21 +195,16 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
 
         """
 
-    @abstractmethod
-    def draw_spider(self, node, wire_drawings, **params) -> None:
+    def _get_wire_color(self, wire_id, **params):
         """
-        Draws the spider node with the list of wires.
-
-        Parameters
-        ----------
-        node: DrawableDiagram
-            the node to be drawn.
-        wire_drawings: list of wires
-            list of wires in the spider
-        params: any
-            Additional parameters.
 
         """
+        print('wire_id-->', wire_id)
+        if not params.get('color_wires') or  wire_id == 0:
+            return '#000000'
+        else:
+            wire_color = WIRE_COLORS[(wire_id - 1) % len(WIRE_COLORS)]
+            return wire_color
 
     @abstractmethod
     def output(self,
diff --git a/lambeq/backend/drawing/mat_backend.py b/lambeq/backend/drawing/mat_backend.py
index 30e901ac..7c892edb 100644
--- a/lambeq/backend/drawing/mat_backend.py
+++ b/lambeq/backend/drawing/mat_backend.py
@@ -72,7 +72,9 @@ def draw_wire(self,
                   bend_in: bool = False,
                   is_leg: bool = False,
                   style: str | None = None,
-                  color: str = 'black') -> None:
+                  color_id: int = 0,
+                  **params) -> None:
+        color = self._get_wire_color(color_id, **params)
         if style == '->':
             self.axis.arrow(
                 *(source + (target[0] - source[0], target[1] - source[1])),
@@ -116,32 +118,22 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
 
         nodes = [node for node in drawable.boxes if drawn_as_spider(node.obj)]
         for node in nodes:
-            if isinstance(node.obj, Spider):
-                self.draw_node(*node.coordinates, **params)
+
             for wire in node.cod_wires:
                 self.draw_wire(node.coordinates,
                                drawable.wire_endpoints[wire].coordinates,
                                bend_out=True,
-                               is_leg=True)
+                               is_leg=True,
+                               color_id=drawable.wire_endpoints[wire].noun_id, **params)
             for wire in node.dom_wires:
+                print('wire->', drawable.wire_endpoints[wire].noun_id)
                 self.draw_wire(drawable.wire_endpoints[wire].coordinates,
                                node.coordinates,
                                bend_in=True,
-                               is_leg=True)
-
-    def draw_spider(self, node, wire_drawings, **params) -> None:
-        if isinstance(node.obj, Spider):
-            self.draw_node(*node.coordinates, **params)
-
-        for wire in wire_drawings:
-            self.draw_wire(
-                wire['start'],
-                wire['end'],
-                bend_out=wire.get('bend_out', False),
-                bend_in=wire.get('bend_in', False),
-                is_leg=wire['is_leg'],
-                color=wire['color']
-            )
+                               is_leg=True,
+                               color_id=drawable.wire_endpoints[wire].noun_id, **params)
+            if isinstance(node.obj, Spider):
+                self.draw_node(*node.coordinates, **params)
 
     def output(self,
                path: str | None = None,

From 84b44565a6b898ffbfa2f9ae073ac5ef72ffdab4 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Fri, 8 Nov 2024 16:34:06 +0000
Subject: [PATCH 03/41] rearrange colors to avoid similar shades comes together

---
 lambeq/backend/drawing/drawing_backend.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index b3c3df70..d2fc9b94 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -31,12 +31,12 @@
 DEFAULT_ASPECT = 'equal'
 
 WIRE_COLORS = [
-    '#9c540e', '#f07e33', '#1d0b66', '#751fa5', '#740127',
-    '#a22417', '#d03b2d', '#fe9100', '#f8c84b', '#5ac358',
-    '#018c76', '#450d06', '#066ee2', '#574cfa', '#7446f2',
-    '#c330b9', '#e2074c', '#d17800', '#f4a940', '#49a141',
-    '#007765', '#398889', '#0252a1', '#3831a0', '#271296',
-    '#a629b3', '#b60539' '#3c8331', '#fea431', '#73b8fd'
+    '#9c540e', '#d03b2d', '#066ee2', '#f4a940', '#271296',
+    '#f07e33', '#fe9100', '#574cfa', '#49a141', '#a629b3',
+    '#1d0b66', '#f8c84b', '#7446f2', '#007765', '#b60539',
+    '#751fa5', '#5ac358', '#c330b9', '#398889', '#3c8331',
+    '#740127', '#018c76', '#e2074c', '#0252a1', '#fea431',
+    '#a22417', '#450d06', '#d17800', '#3831a0', '#73b8fd'
 ]
 
 
@@ -157,7 +157,7 @@ def draw_wire(self,
                   is_leg: bool = False,
                   style: str | None = None,
                   color_id: int = 0,
-                  **params)-> None:
+                  **params) -> None:
         """
         Draws a wire from source to target, possibly with a curve
 

From 8b963d7a2c9d3da75d6bbc8b171067cbec3085da Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Fri, 8 Nov 2024 16:36:43 +0000
Subject: [PATCH 04/41] lint error fixes, flake8 fixes

---
 lambeq/backend/drawing/drawable.py        | 64 ++++++++++++++---------
 lambeq/backend/drawing/drawing.py         |  6 ++-
 lambeq/backend/drawing/drawing_backend.py |  2 +-
 lambeq/backend/drawing/mat_backend.py     | 11 ++--
 4 files changed, 52 insertions(+), 31 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index e88688d6..6e14bfc3 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -95,7 +95,7 @@ class WireEndpoint:
 
     x: float
     y: float
-    noun_id: int = 0 # New attribute for wire noun
+    noun_id: int = 0  # New attribute for wire noun
     parent: Optional['BoxNode'] = None
 
     @property
@@ -340,13 +340,15 @@ def _add_boxnode(self, box: BoxNode) -> int:
         self.boxes.append(box)
         return len(self.boxes) - 1
 
-    def _add_box(self,
-                 scan: list[int],
-                 box: grammar.Box,
-                 off: int,
-                 x_pos: float,
-                 y_pos: float,
-                 input_nouns: list[str]=None) -> tuple[list[int], int, list[str]]:
+    def _add_box(
+            self,
+            scan: list[int],
+            box: grammar.Box,
+            off: int,
+            x_pos: float,
+            y_pos: float,
+            input_nouns: list[str] = None
+    ) -> tuple[list[int], int, list[str]]:
         """Add a box to the graph, creating necessary wire endpoints.
 
         Returns
@@ -374,8 +376,10 @@ def _add_box(self,
         for i, obj in enumerate(box.dom):
             idx = off + i
             nbr_idx = scan[off + i]
-            noun_id = input_nouns[idx] if input_nouns and idx < len(
-                input_nouns) else DrawableDiagramWithFrames.get_noun_id() # Default to black if no input color available
+            noun_id = (input_nouns[idx] if input_nouns
+                       and idx < len(input_nouns)
+                       else DrawableDiagramWithFrames.get_noun_id()
+                       )  # generate new noun id if needed
 
             wire_end = WireEndpoint(WireEndpointType.DOM,
                                     obj=obj,
@@ -393,16 +397,25 @@ def _add_box(self,
             if input_nouns and len(box.dom) > 1:
                 dom_idx_1 = off
                 dom_idx_2 = off + 1
-                input_nouns[dom_idx_1], input_nouns[dom_idx_2] = input_nouns[dom_idx_2], input_nouns[dom_idx_1]
+                input_nouns[dom_idx_1], input_nouns[dom_idx_2] \
+                    = (input_nouns[dom_idx_2], input_nouns[dom_idx_1])
         # if Spider, expand or shrink the noun_ids based on type
         elif isinstance(node.obj, grammar.Spider):
             if len(box.dom) == 1 and len(box.cod) > 1:
-                dom_noun = input_nouns[off] if input_nouns and off < len(input_nouns) else DrawableDiagramWithFrames.get_noun_id()
+                dom_noun = (input_nouns[off] if input_nouns
+                            and off < len(input_nouns)
+                            else DrawableDiagramWithFrames.get_noun_id()
+                            )
                 expanded_colors = [dom_noun] * len(box.cod)
-                input_nouns = input_nouns[:off] + expanded_colors + input_nouns[off + len(box.dom):]
+                input_nouns = (input_nouns[:off] + expanded_colors
+                               + input_nouns[off + len(box.dom):])
             elif len(box.dom) > 1 and len(box.cod) == 1:
-                cod_noun = input_nouns[off] if input_nouns and off < len(input_nouns) else DrawableDiagramWithFrames.get_noun_id()
-                input_nouns = input_nouns[:off] + [cod_noun] + input_nouns[off + len(box.dom):]
+                cod_noun = (input_nouns[off] if input_nouns
+                            and off < len(input_nouns)
+                            else DrawableDiagramWithFrames.get_noun_id()
+                            )
+                input_nouns = (input_nouns[:off] + [cod_noun]
+                               + input_nouns[off + len(box.dom):])
 
         num_output = off+len(box.cod)
         for i in range(num_output):
@@ -423,7 +436,9 @@ def _add_box(self,
                 x = x_pos + X_SPACING * (i - len(box.cod[1:]) / 2)
             y = y_pos - HALF_BOX_HEIGHT
             idx = off + i
-            noun_id = input_nouns[idx] if input_nouns and idx < len(input_nouns) else DrawableDiagramWithFrames.get_noun_id()
+            noun_id = (input_nouns[idx] if input_nouns
+                       and idx < len(input_nouns)
+                       else DrawableDiagramWithFrames.get_noun_id())
             wire_end = WireEndpoint(WireEndpointType.COD,
                                     obj=obj,
                                     x=x,
@@ -435,7 +450,8 @@ def _add_box(self,
             node.add_cod_wire(wire_idx)
 
         # Replace node's dom with its cod in scan
-        return scan[:off] + scan_insert + scan[off + len(box.dom):], box_ind, input_nouns
+        return (scan[:off] + scan_insert + scan[off + len(box.dom):],
+                box_ind, input_nouns)
 
     def _find_box_edges(self,
                         box: grammar.Box,
@@ -819,8 +835,9 @@ class DrawableDiagramWithFrames(DrawableDiagram):
     frame, carrying all information necessary to render it.
 
     """
-    #add counter for Nouns
+    # add counter for Nouns
     noun_id_counter = 1
+
     def _make_space(self,
                     scan: list[int],
                     box: grammar.Box,
@@ -991,7 +1008,6 @@ def calculate_bounds(self) -> tuple[float, float, float, float]:
 
         return min(all_xs), min(all_ys), max(all_xs), max(all_ys)
 
-
     @staticmethod
     def get_noun_id() -> int:
         """Generate a new numerical ID for the noun box/wire."""
@@ -1031,8 +1047,8 @@ def from_diagram(cls,
         num_input = len(diagram.dom)
         input_nouns = []
         for i in range(num_input):
-                new_color = drawable.get_noun_id()
-                input_nouns.append(new_color)
+            new_color = drawable.get_noun_id()
+            input_nouns.append(new_color)
 
         for i, obj in enumerate(diagram.dom):
             wire_end = WireEndpoint(WireEndpointType.INPUT,
@@ -1051,7 +1067,8 @@ def from_diagram(cls,
             # TODO: Debug issues with y coord
             x, y = drawable._make_space(scan, box, off, foliated=foliated)
 
-            scan, box_ind, input_nouns = drawable._add_box(scan, box, off, x, y, input_nouns)
+            scan, box_ind, input_nouns = drawable._add_box(scan, box, off,
+                                                           x, y, input_nouns)
             box_height = BOX_HEIGHT
             # Add drawables for the inside of the frame
             if isinstance(box, grammar.Frame):
@@ -1063,7 +1080,6 @@ def from_diagram(cls,
             min_y = min(min_y, y)
 
         num_output = len(diagram.cod)
-        output_nouns = []
         # Match output nouns with input nouns as much as possible
         for i in range(num_output):
             if i < len(input_nouns):
@@ -1454,7 +1470,7 @@ def _merge_with(self, drawable: 'DrawableDiagramWithFrames') -> None:
         last_wire_endpoint = len(self.wire_endpoints)
 
         for wire_endpoint in drawable.wire_endpoints:
-            # wire_endpoint.noun_id = 0
+            wire_endpoint.noun_id = 0
             self.wire_endpoints.append(wire_endpoint)
 
         for box in drawable.boxes:
diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index 1df272c4..2d0fff1e 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -463,6 +463,7 @@ def _get_box_color(box: grammar.Diagrammable,
 
     return color
 
+
 def _get_wire_color(wire_id):
     if wire_id == 0:
         return '#000000'
@@ -470,6 +471,7 @@ def _get_wire_color(wire_id):
         wire_color = WIRE_COLORS[(wire_id - 1) % len(WIRE_COLORS)]
         return wire_color
 
+
 def _draw_pregroup_state(backend: DrawingBackend,
                          drawable_box: BoxNode,
                          **params) -> DrawingBackend:
@@ -542,8 +544,8 @@ def _draw_wires(backend: DrawingBackend,
             else:
                 wire_color_id = target.noun_id
         # print('wire', wire_color_id)
-        backend.draw_wire(
-            source.coordinates, target.coordinates, color_id=wire_color_id, **params)
+        backend.draw_wire(source.coordinates, target.coordinates,
+                          color_id=wire_color_id, **params)
 
         if (params.get('draw_type_labels', True) and source.kind in
                 {WireEndpointType.INPUT, WireEndpointType.COD}):
diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index d2fc9b94..388ac1a9 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -200,7 +200,7 @@ def _get_wire_color(self, wire_id, **params):
 
         """
         print('wire_id-->', wire_id)
-        if not params.get('color_wires') or  wire_id == 0:
+        if not params.get('color_wires') or wire_id == 0:
             return '#000000'
         else:
             wire_color = WIRE_COLORS[(wire_id - 1) % len(WIRE_COLORS)]
diff --git a/lambeq/backend/drawing/mat_backend.py b/lambeq/backend/drawing/mat_backend.py
index 7c892edb..7a173cec 100644
--- a/lambeq/backend/drawing/mat_backend.py
+++ b/lambeq/backend/drawing/mat_backend.py
@@ -109,8 +109,9 @@ def draw_wire(self,
                     Path.CURVE3,
                 ])
 
-            self.axis.add_patch(PathPatch(
-                path, facecolor='none', linewidth=self.linewidth, edgecolor=color))
+            self.axis.add_patch(PathPatch(path, facecolor='none',
+                                          linewidth=self.linewidth,
+                                          edgecolor=color))
 
         self.max_width = max(self.max_width, source[0], target[0])
 
@@ -124,14 +125,16 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
                                drawable.wire_endpoints[wire].coordinates,
                                bend_out=True,
                                is_leg=True,
-                               color_id=drawable.wire_endpoints[wire].noun_id, **params)
+                               color_id=drawable.wire_endpoints[wire].noun_id,
+                               **params)
             for wire in node.dom_wires:
                 print('wire->', drawable.wire_endpoints[wire].noun_id)
                 self.draw_wire(drawable.wire_endpoints[wire].coordinates,
                                node.coordinates,
                                bend_in=True,
                                is_leg=True,
-                               color_id=drawable.wire_endpoints[wire].noun_id, **params)
+                               color_id=drawable.wire_endpoints[wire].noun_id,
+                               **params)
             if isinstance(node.obj, Spider):
                 self.draw_node(*node.coordinates, **params)
 

From 885c9cbfe69d5edb0b7c3af29f69ddc32f01be51 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Fri, 8 Nov 2024 17:07:53 +0000
Subject: [PATCH 05/41] flake8 fixes

---
 lambeq/backend/drawing/drawable.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 6e14bfc3..27d13497 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -397,8 +397,8 @@ def _add_box(
             if input_nouns and len(box.dom) > 1:
                 dom_idx_1 = off
                 dom_idx_2 = off + 1
-                input_nouns[dom_idx_1], input_nouns[dom_idx_2] \
-                    = (input_nouns[dom_idx_2], input_nouns[dom_idx_1])
+                input_nouns[dom_idx_1], input_nouns[dom_idx_2] = (
+                    input_nouns[dom_idx_2], input_nouns[dom_idx_1])
         # if Spider, expand or shrink the noun_ids based on type
         elif isinstance(node.obj, grammar.Spider):
             if len(box.dom) == 1 and len(box.cod) > 1:
@@ -1046,7 +1046,7 @@ def from_diagram(cls,
         # Generate unique noun_ids for input wires
         num_input = len(diagram.dom)
         input_nouns = []
-        for i in range(num_input):
+        for _ in range(num_input):
             new_color = drawable.get_noun_id()
             input_nouns.append(new_color)
 

From 72b635f94b42d0fbecb8e2dddaca95e8286f33e1 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 14:36:29 +0000
Subject: [PATCH 06/41] inherit add_box to avoid side-effect in non-frame
 diagrams

---
 lambeq/backend/drawing/drawable.py | 194 +++++++++++++++++++----------
 1 file changed, 126 insertions(+), 68 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 27d13497..1505ba75 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -340,15 +340,12 @@ def _add_boxnode(self, box: BoxNode) -> int:
         self.boxes.append(box)
         return len(self.boxes) - 1
 
-    def _add_box(
-            self,
-            scan: list[int],
-            box: grammar.Box,
-            off: int,
-            x_pos: float,
-            y_pos: float,
-            input_nouns: list[str] = None
-    ) -> tuple[list[int], int, list[str]]:
+    def _add_box(self,
+                 scan: list[int],
+                 box: grammar.Box,
+                 off: int,
+                 x_pos: float,
+                 y_pos: float) -> tuple[list[int], int]:
         """Add a box to the graph, creating necessary wire endpoints.
 
         Returns
@@ -357,74 +354,25 @@ def _add_box(
             The new scan of wire endpoints after adding the box
         box_ind : int
             The index of the newly added `BoxNode`
-        list[int]
-            The new order of input_nouns after adding the box
         """
         node = BoxNode(box, x_pos, y_pos)
 
         box_ind = self._add_boxnode(node)
-        num_input = len(box.dom)
-        for i in range(num_input):
-            if i < len(input_nouns):
-                pass
-            else:
-                # If we run out of input nouns, generate new ones
-                new_color = self.get_noun_id()
-                input_nouns.append(new_color)
 
         # Create a node representing each element in the box's domain
         for i, obj in enumerate(box.dom):
-            idx = off + i
             nbr_idx = scan[off + i]
-            noun_id = (input_nouns[idx] if input_nouns
-                       and idx < len(input_nouns)
-                       else DrawableDiagramWithFrames.get_noun_id()
-                       )  # generate new noun id if needed
-
             wire_end = WireEndpoint(WireEndpointType.DOM,
                                     obj=obj,
                                     x=self.wire_endpoints[nbr_idx].x,
-                                    y=y_pos + HALF_BOX_HEIGHT,
-                                    noun_id=noun_id)
+                                    y=y_pos + HALF_BOX_HEIGHT)
 
             wire_idx = self._add_wire_end(wire_end)
             node.add_dom_wire(wire_idx)
             self._add_wire(nbr_idx, wire_idx)
 
         scan_insert = []
-        # if Swap, exchange the noun_ids
-        if isinstance(box, grammar.Swap):
-            if input_nouns and len(box.dom) > 1:
-                dom_idx_1 = off
-                dom_idx_2 = off + 1
-                input_nouns[dom_idx_1], input_nouns[dom_idx_2] = (
-                    input_nouns[dom_idx_2], input_nouns[dom_idx_1])
-        # if Spider, expand or shrink the noun_ids based on type
-        elif isinstance(node.obj, grammar.Spider):
-            if len(box.dom) == 1 and len(box.cod) > 1:
-                dom_noun = (input_nouns[off] if input_nouns
-                            and off < len(input_nouns)
-                            else DrawableDiagramWithFrames.get_noun_id()
-                            )
-                expanded_colors = [dom_noun] * len(box.cod)
-                input_nouns = (input_nouns[:off] + expanded_colors
-                               + input_nouns[off + len(box.dom):])
-            elif len(box.dom) > 1 and len(box.cod) == 1:
-                cod_noun = (input_nouns[off] if input_nouns
-                            and off < len(input_nouns)
-                            else DrawableDiagramWithFrames.get_noun_id()
-                            )
-                input_nouns = (input_nouns[:off] + [cod_noun]
-                               + input_nouns[off + len(box.dom):])
 
-        num_output = off+len(box.cod)
-        for i in range(num_output):
-            if i < len(input_nouns):
-                pass
-            else:
-                # If we run out of input nouns, generate new ones
-                new_color = self.get_noun_id()
-                input_nouns.append(new_color)
         # Create a node representing each element in the box's codomain
         for i, obj in enumerate(box.cod):
 
@@ -435,23 +383,18 @@ def _add_box(
             else:
                 x = x_pos + X_SPACING * (i - len(box.cod[1:]) / 2)
             y = y_pos - HALF_BOX_HEIGHT
-            idx = off + i
-            noun_id = (input_nouns[idx] if input_nouns
-                       and idx < len(input_nouns)
-                       else DrawableDiagramWithFrames.get_noun_id())
+
             wire_end = WireEndpoint(WireEndpointType.COD,
                                     obj=obj,
                                     x=x,
-                                    y=y,
-                                    noun_id=noun_id)
+                                    y=y)
 
             wire_idx = self._add_wire_end(wire_end)
             scan_insert.append(wire_idx)
             node.add_cod_wire(wire_idx)
 
         # Replace node's dom with its cod in scan
-        return (scan[:off] + scan_insert + scan[off + len(box.dom):],
-                box_ind, input_nouns)
+        return scan[:off] + scan_insert + scan[off + len(box.dom):], box_ind
 
     def _find_box_edges(self,
                         box: grammar.Box,
@@ -900,6 +843,121 @@ def _make_space(self,
 
         return x, y
 
+    def _add_box(
+            self,
+            scan: list[int],
+            box: grammar.Box,
+            off: int,
+            x_pos: float,
+            y_pos: float,
+            input_nouns: list[str] = None
+    ) -> tuple[list[int], int, list[str]]:
+        """Add a box to the graph, creating necessary wire endpoints.
+
+        Returns
+        -------
+        list[int]
+            The new scan of wire endpoints after adding the box
+        box_ind : int
+            The index of the newly added `BoxNode`
+        list[int]
+            The new order of input_nouns after adding the box
+        """
+        node = BoxNode(box, x_pos, y_pos)
+
+        box_ind = self._add_boxnode(node)
+        num_input = len(box.dom)
+        input_nouns = input_nouns or []
+        for i in range(num_input):
+            if i < len(input_nouns):
+                pass
+            else:
+                # If we run out of input nouns, generate new ones
+                new_color = self.get_noun_id()
+                input_nouns.append(new_color)
+
+        # Create a node representing each element in the box's domain
+        for i, obj in enumerate(box.dom):
+            idx = off + i
+            nbr_idx = scan[off + i]
+            noun_id = (input_nouns[idx] if input_nouns
+                       and idx < len(input_nouns)
+                       else DrawableDiagramWithFrames.get_noun_id()
+                       )  # generate new noun id if needed
+
+            wire_end = WireEndpoint(WireEndpointType.DOM,
+                                    obj=obj,
+                                    x=self.wire_endpoints[nbr_idx].x,
+                                    y=y_pos + HALF_BOX_HEIGHT,
+                                    noun_id=noun_id)
+
+            wire_idx = self._add_wire_end(wire_end)
+            node.add_dom_wire(wire_idx)
+            self._add_wire(nbr_idx, wire_idx)
+
+        scan_insert = []
+        # if Swap, exchange the noun_ids
+        if isinstance(box, grammar.Swap):
+            if input_nouns and len(box.dom) > 1:
+                dom_idx_1 = off
+                dom_idx_2 = off + 1
+                input_nouns[dom_idx_1], input_nouns[dom_idx_2] = (
+                    input_nouns[dom_idx_2], input_nouns[dom_idx_1])
+        # if Spider, expand or shrink the noun_ids based on type
+        elif isinstance(node.obj, grammar.Spider):
+            if len(box.dom) == 1 and len(box.cod) > 1:
+                dom_noun = (input_nouns[off] if input_nouns
+                            and off < len(input_nouns)
+                            else DrawableDiagramWithFrames.get_noun_id()
+                            )
+                expanded_colors = [dom_noun] * len(box.cod)
+                input_nouns = (input_nouns[:off] + expanded_colors
+                               + input_nouns[off + len(box.dom):])
+            elif len(box.dom) > 1 and len(box.cod) == 1:
+                cod_noun = (input_nouns[off] if input_nouns
+                            and off < len(input_nouns)
+                            else DrawableDiagramWithFrames.get_noun_id()
+                            )
+                input_nouns = (input_nouns[:off] + [cod_noun]
+                               + input_nouns[off + len(box.dom):])
+
+        num_output = off+len(box.cod)
+        for i in range(num_output):
+            if i < len(input_nouns):
+                pass
+            else:
+                # If we run out of input nouns, generate new ones
+                new_color = self.get_noun_id()
+                input_nouns.append(new_color)
+        # Create a node representing each element in the box's codomain
+        for i, obj in enumerate(box.cod):
+
+            # If the box is a quantum gate, retain x coordinate of wires
+            if box.category == quantum and len(box.dom) == len(box.cod):
+                nbr_idx = scan[off + i]
+                x = self.wire_endpoints[nbr_idx].x
+            else:
+                x = x_pos + X_SPACING * (i - len(box.cod[1:]) / 2)
+            y = y_pos - HALF_BOX_HEIGHT
+            idx = off + i
+            noun_id = (input_nouns[idx] if input_nouns
+                       and idx < len(input_nouns)
+                       else DrawableDiagramWithFrames.get_noun_id())
+            wire_end = WireEndpoint(WireEndpointType.COD,
+                                    obj=obj,
+                                    x=x,
+                                    y=y,
+                                    noun_id=noun_id)
+
+            wire_idx = self._add_wire_end(wire_end)
+            scan_insert.append(wire_idx)
+            node.add_cod_wire(wire_idx)
+
+        # Replace node's dom with its cod in scan
+        return (scan[:off] + scan_insert + scan[off + len(box.dom):],
+                box_ind, input_nouns)
+
+
     def _make_space_for_frame(self,
                               scan: list[int],
                               off: int,
@@ -1046,7 +1104,7 @@ def from_diagram(cls,
         # Generate unique noun_ids for input wires
         num_input = len(diagram.dom)
         input_nouns = []
-        for _ in range(num_input):
+        for i in range(num_input):
             new_color = drawable.get_noun_id()
             input_nouns.append(new_color)
 

From 9dbcc40cf9883a92b5311ab1da3ce65bc1d3a66d Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 14:37:24 +0000
Subject: [PATCH 07/41] remove debug logs

---
 lambeq/backend/drawing/drawing.py         | 1 -
 lambeq/backend/drawing/drawing_backend.py | 1 -
 lambeq/backend/drawing/mat_backend.py     | 1 -
 3 files changed, 3 deletions(-)

diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index 2d0fff1e..c4c27204 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -543,7 +543,6 @@ def _draw_wires(backend: DrawingBackend,
                 wire_color_id = source.noun_id
             else:
                 wire_color_id = target.noun_id
-        # print('wire', wire_color_id)
         backend.draw_wire(source.coordinates, target.coordinates,
                           color_id=wire_color_id, **params)
 
diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index 388ac1a9..04c97067 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -199,7 +199,6 @@ def _get_wire_color(self, wire_id, **params):
         """
 
         """
-        print('wire_id-->', wire_id)
         if not params.get('color_wires') or wire_id == 0:
             return '#000000'
         else:
diff --git a/lambeq/backend/drawing/mat_backend.py b/lambeq/backend/drawing/mat_backend.py
index 7a173cec..52062405 100644
--- a/lambeq/backend/drawing/mat_backend.py
+++ b/lambeq/backend/drawing/mat_backend.py
@@ -128,7 +128,6 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
                                color_id=drawable.wire_endpoints[wire].noun_id,
                                **params)
             for wire in node.dom_wires:
-                print('wire->', drawable.wire_endpoints[wire].noun_id)
                 self.draw_wire(drawable.wire_endpoints[wire].coordinates,
                                node.coordinates,
                                bend_in=True,

From 3a8f66d83e5aaf31731f79665bab844039fac98b Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 14:38:55 +0000
Subject: [PATCH 08/41] add color params to draw method in Tikzit backend

---
 lambeq/backend/drawing/tikz_backend.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index dded2fa6..6f58b969 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -122,7 +122,11 @@ def draw_wire(self,
                   bend_out: bool = False,
                   bend_in: bool = False,
                   is_leg: bool = False,
-                  style: str | None = None) -> None:
+                  style: str | None = None,
+                  color_id: int = 0,
+                  **params) -> None:
+
+        color = self._get_wire_color(color_id, **params)
         out = (-90 if not bend_out or source[0] == target[0]
                else (180 if source[0] > target[0] else 0))
         inp = (90 if not bend_in or source[0] == target[0]
@@ -178,11 +182,15 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
             for wire in node.cod_wires:
                 self.draw_wire(node.coordinates,
                                drawable.wire_endpoints[wire].coordinates,
-                               bend_out=True)
+                               bend_out=True,
+                               color_id=drawable.wire_endpoints[wire].noun_id,
+                               **params)
             for wire in node.dom_wires:
                 self.draw_wire(drawable.wire_endpoints[wire].coordinates,
                                node.coordinates,
-                               bend_in=True)
+                               bend_in=True,
+                               color_id=drawable.wire_endpoints[wire].noun_id,
+                               **params)
 
     def output(self, path=None, show=True, **params) -> None:
         baseline = params.get('baseline', 0)

From 3ef64a5780896c8e41cf563a8df13be5fe5bf119 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 14:52:58 +0000
Subject: [PATCH 09/41] lint error fixes

---
 lambeq/backend/drawing/drawable.py     | 5 ++---
 lambeq/backend/drawing/tikz_backend.py | 2 +-
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 1505ba75..e43ed7f1 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -850,8 +850,8 @@ def _add_box(
             off: int,
             x_pos: float,
             y_pos: float,
-            input_nouns: list[str] = None
-    ) -> tuple[list[int], int, list[str]]:
+            input_nouns: list[int] = None
+    ) -> tuple[list[int], int, list[int]]:
         """Add a box to the graph, creating necessary wire endpoints.
 
         Returns
@@ -957,7 +957,6 @@ def _add_box(
         return (scan[:off] + scan_insert + scan[off + len(box.dom):],
                 box_ind, input_nouns)
 
-
     def _make_space_for_frame(self,
                               scan: list[int],
                               off: int,
diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 6f58b969..42cc4008 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -126,7 +126,7 @@ def draw_wire(self,
                   color_id: int = 0,
                   **params) -> None:
 
-        color = self._get_wire_color(color_id, **params)
+        # color = self._get_wire_color(color_id, **params)
         out = (-90 if not bend_out or source[0] == target[0]
                else (180 if source[0] > target[0] else 0))
         inp = (90 if not bend_in or source[0] == target[0]

From 1a1f2a04c8982adb4466a5e8a3d2248e6436375f Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 15:24:11 +0000
Subject: [PATCH 10/41] mypy suggestion fixes

---
 lambeq/backend/drawing/drawable.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index e43ed7f1..fb518ffd 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -843,14 +843,14 @@ def _make_space(self,
 
         return x, y
 
-    def _add_box(
+    def _add_box_with_nouns(
             self,
             scan: list[int],
             box: grammar.Box,
             off: int,
             x_pos: float,
             y_pos: float,
-            input_nouns: list[int] = None
+            input_nouns: list[int] = []
     ) -> tuple[list[int], int, list[int]]:
         """Add a box to the graph, creating necessary wire endpoints.
 
@@ -1103,7 +1103,7 @@ def from_diagram(cls,
         # Generate unique noun_ids for input wires
         num_input = len(diagram.dom)
         input_nouns = []
-        for i in range(num_input):
+        for _ in range(num_input):
             new_color = drawable.get_noun_id()
             input_nouns.append(new_color)
 
@@ -1124,8 +1124,8 @@ def from_diagram(cls,
             # TODO: Debug issues with y coord
             x, y = drawable._make_space(scan, box, off, foliated=foliated)
 
-            scan, box_ind, input_nouns = drawable._add_box(scan, box, off,
-                                                           x, y, input_nouns)
+            scan, box_ind, input_nouns = drawable._add_box_with_nouns(
+                                            scan, box, off, x, y, input_nouns)
             box_height = BOX_HEIGHT
             # Add drawables for the inside of the frame
             if isinstance(box, grammar.Frame):

From 94f6dbd0538b64f9b0030f9af3d5207343390daa Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 15:31:27 +0000
Subject: [PATCH 11/41] demo on simple-book notebook

---
 tests/backend/simple-books-frame-drawing.html | 8286 +++++++++++++++++
 1 file changed, 8286 insertions(+)
 create mode 100644 tests/backend/simple-books-frame-drawing.html

diff --git a/tests/backend/simple-books-frame-drawing.html b/tests/backend/simple-books-frame-drawing.html
new file mode 100644
index 00000000..f2dc8e24
--- /dev/null
+++ b/tests/backend/simple-books-frame-drawing.html
@@ -0,0 +1,8286 @@
+<!DOCTYPE html>
+
+<html lang="en">
+<head><meta charset="utf-8"/>
+<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
+<title>simple-books-frame-drawing</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<style type="text/css">
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+<style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+ * Mozilla scrollbar styling
+ */
+
+/* use standard opaque scrollbars for most nodes */
+[data-jp-theme-scrollbars='true'] {
+  scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
+    var(--jp-scrollbar-background-color);
+}
+
+/* for code nodes, use a transparent style of scrollbar. These selectors
+ * will match lower in the tree, and so will override the above */
+[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
+[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
+  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
+}
+
+/* tiny scrollbar */
+
+.jp-scrollbar-tiny {
+  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
+  scrollbar-width: thin;
+}
+
+/* tiny scrollbar */
+
+.jp-scrollbar-tiny::-webkit-scrollbar,
+.jp-scrollbar-tiny::-webkit-scrollbar-corner {
+  background-color: transparent;
+  height: 4px;
+  width: 4px;
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-thumb {
+  background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal {
+  border-left: 0 solid transparent;
+  border-right: 0 solid transparent;
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-track:vertical {
+  border-top: 0 solid transparent;
+  border-bottom: 0 solid transparent;
+}
+
+/*
+ * Lumino
+ */
+
+.lm-ScrollBar[data-orientation='horizontal'] {
+  min-height: 16px;
+  max-height: 16px;
+  min-width: 45px;
+  border-top: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] {
+  min-width: 16px;
+  max-width: 16px;
+  min-height: 45px;
+  border-left: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar-button {
+  background-color: #f0f0f0;
+  background-position: center center;
+  min-height: 15px;
+  max-height: 15px;
+  min-width: 15px;
+  max-width: 15px;
+}
+
+.lm-ScrollBar-button:hover {
+  background-color: #dadada;
+}
+
+.lm-ScrollBar-button.lm-mod-active {
+  background-color: #cdcdcd;
+}
+
+.lm-ScrollBar-track {
+  background: #f0f0f0;
+}
+
+.lm-ScrollBar-thumb {
+  background: #cdcdcd;
+}
+
+.lm-ScrollBar-thumb:hover {
+  background: #bababa;
+}
+
+.lm-ScrollBar-thumb.lm-mod-active {
+  background: #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
+  height: 100%;
+  min-width: 15px;
+  border-left: 1px solid #a0a0a0;
+  border-right: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
+  width: 100%;
+  min-height: 15px;
+  border-top: 1px solid #a0a0a0;
+  border-bottom: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-left);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-right);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-up);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-down);
+  background-size: 17px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-Widget {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+}
+
+.lm-Widget.lm-mod-hidden {
+  display: none !important;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.lm-AccordionPanel[data-orientation='horizontal'] > .lm-AccordionPanel-title {
+  /* Title is rotated for horizontal accordion panel using CSS */
+  display: block;
+  transform-origin: top left;
+  transform: rotate(-90deg) translate(-100%);
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  display: flex;
+  flex-direction: column;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-CommandPalette-search {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  min-height: 0;
+  overflow: auto;
+  list-style-type: none;
+}
+
+.lm-CommandPalette-header {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.lm-CommandPalette-item {
+  display: flex;
+  flex-direction: row;
+}
+
+.lm-CommandPalette-itemIcon {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemContent {
+  flex: 1 1 auto;
+  overflow: hidden;
+}
+
+.lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemLabel {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.lm-close-icon {
+  border: 1px solid transparent;
+  background-color: transparent;
+  position: absolute;
+  z-index: 1;
+  right: 3%;
+  top: 0;
+  bottom: 0;
+  margin: auto;
+  padding: 7px 0;
+  display: none;
+  vertical-align: middle;
+  outline: 0;
+  cursor: pointer;
+}
+.lm-close-icon:after {
+  content: 'X';
+  display: block;
+  width: 15px;
+  height: 15px;
+  text-align: center;
+  color: #000;
+  font-weight: normal;
+  font-size: 12px;
+  cursor: pointer;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-DockPanel {
+  z-index: 0;
+}
+
+.lm-DockPanel-widget {
+  z-index: 0;
+}
+
+.lm-DockPanel-tabBar {
+  z-index: 1;
+}
+
+.lm-DockPanel-handle {
+  z-index: 2;
+}
+
+.lm-DockPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-DockPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+.lm-DockPanel-handle[data-orientation='horizontal'] {
+  cursor: ew-resize;
+}
+
+.lm-DockPanel-handle[data-orientation='vertical'] {
+  cursor: ns-resize;
+}
+
+.lm-DockPanel-handle[data-orientation='horizontal']:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+.lm-DockPanel-handle[data-orientation='vertical']:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+.lm-DockPanel-overlay {
+  z-index: 3;
+  box-sizing: border-box;
+  pointer-events: none;
+}
+
+.lm-DockPanel-overlay.lm-mod-hidden {
+  display: none !important;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-Menu {
+  z-index: 10000;
+  position: absolute;
+  white-space: nowrap;
+  overflow-x: hidden;
+  overflow-y: auto;
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-Menu-content {
+  margin: 0;
+  padding: 0;
+  display: table;
+  list-style-type: none;
+}
+
+.lm-Menu-item {
+  display: table-row;
+}
+
+.lm-Menu-item.lm-mod-hidden,
+.lm-Menu-item.lm-mod-collapsed {
+  display: none !important;
+}
+
+.lm-Menu-itemIcon,
+.lm-Menu-itemSubmenuIcon {
+  display: table-cell;
+  text-align: center;
+}
+
+.lm-Menu-itemLabel {
+  display: table-cell;
+  text-align: left;
+}
+
+.lm-Menu-itemShortcut {
+  display: table-cell;
+  text-align: right;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-MenuBar {
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-MenuBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: row;
+  list-style-type: none;
+}
+
+.lm-MenuBar-item {
+  box-sizing: border-box;
+}
+
+.lm-MenuBar-itemIcon,
+.lm-MenuBar-itemLabel {
+  display: inline-block;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-ScrollBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-ScrollBar[data-orientation='horizontal'] {
+  flex-direction: row;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] {
+  flex-direction: column;
+}
+
+.lm-ScrollBar-button {
+  box-sizing: border-box;
+  flex: 0 0 auto;
+}
+
+.lm-ScrollBar-track {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+  flex: 1 1 auto;
+}
+
+.lm-ScrollBar-thumb {
+  box-sizing: border-box;
+  position: absolute;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-SplitPanel-child {
+  z-index: 0;
+}
+
+.lm-SplitPanel-handle {
+  z-index: 1;
+}
+
+.lm-SplitPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-SplitPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
+  cursor: ew-resize;
+}
+
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
+  cursor: ns-resize;
+}
+
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-TabBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-TabBar[data-orientation='horizontal'] {
+  flex-direction: row;
+  align-items: flex-end;
+}
+
+.lm-TabBar[data-orientation='vertical'] {
+  flex-direction: column;
+  align-items: flex-end;
+}
+
+.lm-TabBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex: 1 1 auto;
+  list-style-type: none;
+}
+
+.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
+  flex-direction: row;
+}
+
+.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
+  flex-direction: column;
+}
+
+.lm-TabBar-tab {
+  display: flex;
+  flex-direction: row;
+  box-sizing: border-box;
+  overflow: hidden;
+  touch-action: none; /* Disable native Drag/Drop */
+}
+
+.lm-TabBar-tabIcon,
+.lm-TabBar-tabCloseIcon {
+  flex: 0 0 auto;
+}
+
+.lm-TabBar-tabLabel {
+  flex: 1 1 auto;
+  overflow: hidden;
+  white-space: nowrap;
+}
+
+.lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.lm-TabBar-tab.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-TabBar-addButton.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
+  position: relative;
+}
+
+.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
+  left: 0;
+  transition: left 150ms ease;
+}
+
+.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
+  top: 0;
+  transition: top 150ms ease;
+}
+
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
+  transition: none;
+}
+
+.lm-TabBar-tabLabel .lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing: border-box;
+  background: inherit;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-TabPanel-tabBar {
+  z-index: 1;
+}
+
+.lm-TabPanel-stackedPanel {
+  z-index: 0;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapse {
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+
+.jp-Collapse-header {
+  padding: 1px 12px;
+  background-color: var(--jp-layout-color1);
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  text-transform: uppercase;
+  user-select: none;
+}
+
+.jp-Collapser-icon {
+  height: 16px;
+}
+
+.jp-Collapse-header-collapsed .jp-Collapser-icon {
+  transform: rotate(-90deg);
+  margin: auto 0;
+}
+
+.jp-Collapser-title {
+  line-height: 25px;
+}
+
+.jp-Collapse-contents {
+  padding: 0 12px;
+  background-color: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+/* Icons urls */
+
+:root {
+  --jp-icon-add-above: url(data:image/svg+xml;base64,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);
+  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
+  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-bell: url(data:image/svg+xml;base64,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);
+  --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
+  --jp-icon-bug: url(data:image/svg+xml;base64,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);
+  --jp-icon-build: url(data:image/svg+xml;base64,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);
+  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-up-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iMTUuNCwxMy4zIDkuOSw3LjcgNC40LDEzLjIgMy42LDEyLjUgOS45LDYuMyAxNi4xLDEyLjYgIi8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-up: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik01LjIsMTAuNUw5LDYuOGwzLjgsMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-case-sensitive: url(data:image/svg+xml;base64,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);
+  --jp-icon-check: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik05IDE2LjE3TDQuODMgMTJsLTEuNDIgMS40MUw5IDE5IDIxIDdsLTEuNDEtMS40MXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-circle-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDJDNi40NyAyIDIgNi40NyAyIDEyczQuNDcgMTAgMTAgMTAgMTAtNC40NyAxMC0xMFMxNy41MyAyIDEyIDJ6bTAgMThjLTQuNDEgMC04LTMuNTktOC04czMuNTktOCA4LTggOCAzLjU5IDggOC0zLjU5IDgtOCA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-clear: url(data:image/svg+xml;base64,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);
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+  --jp-icon-code-check: url(data:image/svg+xml;base64,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);
+  --jp-icon-code: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTExLjQgMTguNkw2LjggMTRMMTEuNCA5LjRMMTAgOEw0IDE0TDEwIDIwTDExLjQgMTguNlpNMTYuNiAxOC42TDIxLjIgMTRMMTYuNiA5LjRMMTggOEwyNCAxNEwxOCAyMEwxNi42IDE4LjZWMTguNloiLz4KCTwvZz4KPC9zdmc+Cg==);
+  --jp-icon-collapse-all: url(data:image/svg+xml;base64,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);
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+  --jp-icon-copyright: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGVuYWJsZS1iYWNrZ3JvdW5kPSJuZXcgMCAwIDI0IDI0IiBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCI+CiAgPGcgY2xhc3M9ImpwLWljb24zIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0xMS44OCw5LjE0YzEuMjgsMC4wNiwxLjYxLDEuMTUsMS42MywxLjY2aDEuNzljLTAuMDgtMS45OC0xLjQ5LTMuMTktMy40NS0zLjE5QzkuNjQsNy42MSw4LDksOCwxMi4xNCBjMCwxLjk0LDAuOTMsNC4yNCwzLjg0LDQuMjRjMi4yMiwwLDMuNDEtMS42NSwzLjQ0LTIuOTVoLTEuNzljLTAuMDMsMC41OS0wLjQ1LDEuMzgtMS42MywxLjQ0QzEwLjU1LDE0LjgzLDEwLDEzLjgxLDEwLDEyLjE0IEMxMCw5LjI1LDExLjI4LDkuMTYsMTEuODgsOS4xNHogTTEyLDJDNi40OCwyLDIsNi40OCwyLDEyczQuNDgsMTAsMTAsMTBzMTAtNC40OCwxMC0xMFMxNy41MiwyLDEyLDJ6IE0xMiwyMGMtNC40MSwwLTgtMy41OS04LTggczMuNTktOCw4LThzOCwzLjU5LDgsOFMxNi40MSwyMCwxMiwyMHoiLz4KICA8L2c+Cjwvc3ZnPgo=);
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+  --jp-icon-ellipses: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iNSIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxOSIgY3k9IjEyIiByPSIyIi8+CiAgPC9nPgo8L3N2Zz4K);
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+  --jp-icon-extension: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwLjUgMTFIMTlWN2MwLTEuMS0uOS0yLTItMmgtNFYzLjVDMTMgMi4xMiAxMS44OCAxIDEwLjUgMVM4IDIuMTIgOCAzLjVWNUg0Yy0xLjEgMC0xLjk5LjktMS45OSAydjMuOEgzLjVjMS40OSAwIDIuNyAxLjIxIDIuNyAyLjdzLTEuMjEgMi43LTIuNyAyLjdIMlYyMGMwIDEuMS45IDIgMiAyaDMuOHYtMS41YzAtMS40OSAxLjIxLTIuNyAyLjctMi43IDEuNDkgMCAyLjcgMS4yMSAyLjcgMi43VjIySDE3YzEuMSAwIDItLjkgMi0ydi00aDEuNWMxLjM4IDAgMi41LTEuMTIgMi41LTIuNVMyMS44OCAxMSAyMC41IDExeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-fast-forward: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTQgMThsOC41LTZMNCA2djEyem05LTEydjEybDguNS02TDEzIDZ6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-file-upload: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTZoNnYtNmg0bC03LTctNyA3aDR6bS00IDJoMTR2Mkg1eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-file: url(data:image/svg+xml;base64,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);
+  --jp-icon-filter-dot: url(data:image/svg+xml;base64,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);
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+  --jp-icon-filter: url(data:image/svg+xml;base64,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);
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+  --jp-icon-home: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjRweCIgdmlld0JveD0iMCAwIDI0IDI0IiB3aWR0aD0iMjRweCIgZmlsbD0iIzAwMDAwMCI+CiAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPjxwYXRoIGNsYXNzPSJqcC1pY29uMyBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xMCAyMHYtNmg0djZoNXYtOGgzTDEyIDMgMiAxMmgzdjh6Ii8+Cjwvc3ZnPgo=);
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+  --jp-icon-info: url(data:image/svg+xml;base64,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);
+  --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaW5zcGVjdG9yLWljb24tY29sb3IganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
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+  --jp-icon-kernel: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzYxNjE2MSIgZD0iTTE1IDlIOXY2aDZWOXptLTIgNGgtMnYtMmgydjJ6bTgtMlY5aC0yVjdjMC0xLjEtLjktMi0yLTJoLTJWM2gtMnYyaC0yVjNIOXYySDdjLTEuMSAwLTIgLjktMiAydjJIM3YyaDJ2MkgzdjJoMnYyYzAgMS4xLjkgMiAyIDJoMnYyaDJ2LTJoMnYyaDJ2LTJoMmMxLjEgMCAyLS45IDItMnYtMmgydi0yaC0ydi0yaDJ6bS00IDZIN1Y3aDEwdjEweiIvPgo8L3N2Zz4K);
+  --jp-icon-keyboard: url(data:image/svg+xml;base64,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);
+  --jp-icon-launch: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMzIgMzIiIHdpZHRoPSIzMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yNiwyOEg2YTIuMDAyNywyLjAwMjcsMCwwLDEtMi0yVjZBMi4wMDI3LDIuMDAyNywwLDAsMSw2LDRIMTZWNkg2VjI2SDI2VjE2aDJWMjZBMi4wMDI3LDIuMDAyNywwLDAsMSwyNiwyOFoiLz4KICAgIDxwb2x5Z29uIHBvaW50cz0iMjAgMiAyMCA0IDI2LjU4NiA0IDE4IDEyLjU4NiAxOS40MTQgMTQgMjggNS40MTQgMjggMTIgMzAgMTIgMzAgMiAyMCAyIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-launcher: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkgMTlINVY1aDdWM0g1YTIgMiAwIDAwLTIgMnYxNGEyIDIgMCAwMDIgMmgxNGMxLjEgMCAyLS45IDItMnYtN2gtMnY3ek0xNCAzdjJoMy41OWwtOS44MyA5LjgzIDEuNDEgMS40MUwxOSA2LjQxVjEwaDJWM2gtN3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-line-form: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGZpbGw9IndoaXRlIiBkPSJNNS44OCA0LjEyTDEzLjc2IDEybC03Ljg4IDcuODhMOCAyMmwxMC0xMEw4IDJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-link: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMuOSAxMmMwLTEuNzEgMS4zOS0zLjEgMy4xLTMuMWg0VjdIN2MtMi43NiAwLTUgMi4yNC01IDVzMi4yNCA1IDUgNWg0di0xLjlIN2MtMS43MSAwLTMuMS0xLjM5LTMuMS0zLjF6TTggMTNoOHYtMkg4djJ6bTktNmgtNHYxLjloNGMxLjcxIDAgMy4xIDEuMzkgMy4xIDMuMXMtMS4zOSAzLjEtMy4xIDMuMWgtNFYxN2g0YzIuNzYgMCA1LTIuMjQgNS01cy0yLjI0LTUtNS01eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4K);
+  --jp-icon-markdown: url(data:image/svg+xml;base64,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);
+  --jp-icon-move-down: url(data:image/svg+xml;base64,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);
+  --jp-icon-move-up: url(data:image/svg+xml;base64,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);
+  --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-not-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtbm90ZWJvb2staWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-offline-bolt: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDIuMDJjLTUuNTEgMC05Ljk4IDQuNDctOS45OCA5Ljk4czQuNDcgOS45OCA5Ljk4IDkuOTggOS45OC00LjQ3IDkuOTgtOS45OFMxNy41MSAyLjAyIDEyIDIuMDJ6TTExLjQ4IDIwdi02LjI2SDhMMTMgNHY2LjI2aDMuMzVMMTEuNDggMjB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-palette: url(data:image/svg+xml;base64,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);
+  --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-pdf: url(data:image/svg+xml;base64,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);
+  --jp-icon-python: url(data:image/svg+xml;base64,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);
+  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
+  --jp-icon-react: url(data:image/svg+xml;base64,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);
+  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-regex: url(data:image/svg+xml;base64,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);
+  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-search: url(data:image/svg+xml;base64,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);
+  --jp-icon-settings: url(data:image/svg+xml;base64,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);
+  --jp-icon-share: url(data:image/svg+xml;base64,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);
+  --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-tag: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjgiIGhlaWdodD0iMjgiIHZpZXdCb3g9IjAgMCA0MyAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTI4LjgzMzIgMTIuMzM0TDMyLjk5OTggMTYuNTAwN0wzNy4xNjY1IDEyLjMzNEgyOC44MzMyWiIvPgoJCTxwYXRoIGQ9Ik0xNi4yMDk1IDIxLjYxMDRDMTUuNjg3MyAyMi4xMjk5IDE0Ljg0NDMgMjIuMTI5OSAxNC4zMjQ4IDIxLjYxMDRMNi45ODI5IDE0LjcyNDVDNi41NzI0IDE0LjMzOTQgNi4wODMxMyAxMy42MDk4IDYuMDQ3ODYgMTMuMDQ4MkM1Ljk1MzQ3IDExLjUyODggNi4wMjAwMiA4LjYxOTQ0IDYuMDY2MjEgNy4wNzY5NUM2LjA4MjgxIDYuNTE0NzcgNi41NTU0OCA2LjA0MzQ3IDcuMTE4MDQgNi4wMzA1NUM5LjA4ODYzIDUuOTg0NzMgMTMuMjYzOCA1LjkzNTc5IDEzLjY1MTggNi4zMjQyNUwyMS43MzY5IDEzLjYzOUMyMi4yNTYgMTQuMTU4NSAyMS43ODUxIDE1LjQ3MjQgMjEuMjYyIDE1Ljk5NDZMMTYuMjA5NSAyMS42MTA0Wk05Ljc3NTg1IDguMjY1QzkuMzM1NTEgNy44MjU2NiA4LjYyMzUxIDcuODI1NjYgOC4xODI4IDguMjY1QzcuNzQzNDYgOC43MDU3MSA3Ljc0MzQ2IDkuNDE3MzMgOC4xODI4IDkuODU2NjdDOC42MjM4MiAxMC4yOTY0IDkuMzM1ODIgMTAuMjk2NCA5Ljc3NTg1IDkuODU2NjdDMTAuMjE1NiA5LjQxNzMzIDEwLjIxNTYgOC43MDUzMyA5Ljc3NTg1IDguMjY1WiIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-terminal: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0IiA+CiAgICA8cmVjdCBjbGFzcz0ianAtdGVybWluYWwtaWNvbi1iYWNrZ3JvdW5kLWNvbG9yIGpwLWljb24tc2VsZWN0YWJsZSIgd2lkdGg9IjIwIiBoZWlnaHQ9IjIwIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgyIDIpIiBmaWxsPSIjMzMzMzMzIi8+CiAgICA8cGF0aCBjbGFzcz0ianAtdGVybWluYWwtaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUtaW52ZXJzZSIgZD0iTTUuMDU2NjQgOC43NjE3MkM1LjA1NjY0IDguNTk3NjYgNS4wMzEyNSA4LjQ1MzEyIDQuOTgwNDcgOC4zMjgxMkM0LjkzMzU5IDguMTk5MjIgNC44NTU0NyA4LjA4MjAzIDQuNzQ2MDkgNy45NzY1NkM0LjY0MDYyIDcuODcxMDkgNC41IDcuNzc1MzkgNC4zMjQyMiA3LjY4OTQ1QzQuMTUyMzQgNy41OTk2MSAzLjk0MzM2IDcuNTExNzIgMy42OTcyNyA3LjQyNTc4QzMuMzAyNzMgNy4yODUxNiAyLjk0MzM2IDcuMTM2NzIgMi42MTkxNCA2Ljk4MDQ3QzIuMjk0OTIgNi44MjQyMiAyLjAxNzU4IDYuNjQyNTggMS43ODcxMSA2LjQzNTU1QzEuNTYwNTUgNi4yMjg1MiAxLjM4NDc3IDUuOTg4MjggMS4yNTk3NyA1LjcxNDg0QzEuMTM0NzcgNS40Mzc1IDEuMDcyMjcgNS4xMDkzOCAxLjA3MjI3IDQuNzMwNDdDMS4wNzIyNyA0LjM5ODQ0IDEuMTI4OTEgNC4wOTU3IDEuMjQyMTkgMy44MjIyN0MxLjM1NTQ3IDMuNTQ0OTIgMS41MTU2MiAzLjMwNDY5IDEuNzIyNjYgMy4xMDE1NkMxLjkyOTY5IDIuODk4NDQgMi4xNzk2OSAyLjczNDM3IDIuNDcyNjYgMi42MDkzOEMyLjc2NTYyIDIuNDg0MzggMy4wOTE4IDIuNDA0MyAzLjQ1MTE3IDIuMzY5MTRWMS4xMDkzOEg0LjM4ODY3VjIuMzgwODZDNC43NDAyMyAyLjQyNzczIDUuMDU2NjQgMi41MjM0NCA1LjMzNzg5IDIuNjY3OTdDNS42MTkxNCAyLjgxMjUgNS44NTc0MiAzLjAwMTk1IDYuMDUyNzMgMy4yMzYzM0M2LjI1MTk1IDMuNDY2OCA2LjQwNDMgMy43NDAyMyA2LjUwOTc3IDQuMDU2NjRDNi42MTkxNCA0LjM2OTE0IDYuNjczODMgNC43MjA3IDYuNjczODMgNS4xMTEzM0g1LjA0NDkyQzUuMDQ0OTIgNC42Mzg2NyA0LjkzNzUgNC4yODEyNSA0LjcyMjY2IDQuMDM5MDZDNC41MDc4MSAzLjc5Mjk3IDQuMjE2OCAzLjY2OTkyIDMuODQ5NjEgMy42Njk5MkMzLjY1MDM5IDMuNjY5OTIgMy40NzY1NiAzLjY5NzI3IDMuMzI4MTIgMy43NTE5NUMzLjE4MzU5IDMuODAyNzMgMy4wNjQ0NSAzLjg3Njk1IDIuOTcwNyAzLjk3NDYxQzIuODc2OTUgNC4wNjgzNiAyLjgwNjY0IDQuMTc5NjkgMi43NTk3NyA0LjMwODU5QzIuNzE2OCA0LjQzNzUgMi42OTUzMSA0LjU3ODEyIDIuNjk1MzEgNC43MzA0N0MyLjY5NTMxIDQuODgyODEgMi43MTY4IDUuMDE5NTMgMi43NTk3NyA1LjE0MDYyQzIuODA2NjQgNS4yNTc4MSAyLjg4MjgxIDUuMzY3MTkgMi45ODgyOCA1LjQ2ODc1QzMuMDk3NjYgNS41NzAzMSAzLjI0MDIzIDUuNjY3OTcgMy40MTYwMiA1Ljc2MTcyQzMuNTkxOCA1Ljg1MTU2IDMuODEwNTUgNS45NDMzNiA0LjA3MjI3IDYuMDM3MTFDNC40NjY4IDYuMTg1NTUgNC44MjQyMiA2LjMzOTg0IDUuMTQ0NTMgNi41QzUuNDY0ODQgNi42NTYyNSA1LjczODI4IDYuODM5ODQgNS45NjQ4NCA3LjA1MDc4QzYuMTk1MzEgNy4yNTc4MSA2LjM3MTA5IDcuNSA2LjQ5MjE5IDcuNzc3MzRDNi42MTcxOSA4LjA1MDc4IDYuNjc5NjkgOC4zNzUgNi42Nzk2OSA4Ljc1QzYuNjc5NjkgOS4wOTM3NSA2LjYyMzA1IDkuNDA0MyA2LjUwOTc3IDkuNjgxNjRDNi4zOTY0OCA5Ljk1NTA4IDYuMjM0MzggMTAuMTkxNCA2LjAyMzQ0IDEwLjM5MDZDNS44MTI1IDEwLjU4OTggNS41NTg1OSAxMC43NSA1LjI2MTcyIDEwLjg3MTFDNC45NjQ4NCAxMC45ODgzIDQuNjMyODEgMTEuMDY0NSA0LjI2NTYyIDExLjA5OTZWMTIuMjQ4SDMuMzMzOThWMTEuMDk5NkMzLjAwMTk1IDExLjA2ODQgMi42Nzk2OSAxMC45OTYxIDIuMzY3MTkgMTAuODgyOEMyLjA1NDY5IDEwLjc2NTYgMS43NzczNCAxMC41OTc3IDEuNTM1MTYgMTAuMzc4OUMxLjI5Njg4IDEwLjE2MDIgMS4xMDU0NyA5Ljg4NDc3IDAuOTYwOTM4IDkuNTUyNzNDMC44MTY0MDYgOS4yMTY4IDAuNzQ0MTQxIDguODE0NDUgMC43NDQxNDEgOC4zNDU3SDIuMzc4OTFDMi4zNzg5MSA4LjYyNjk1IDIuNDE5OTIgOC44NjMyOCAyLjUwMTk1IDkuMDU0NjlDMi41ODM5OCA5LjI0MjE5IDIuNjg5NDUgOS4zOTI1OCAyLjgxODM2IDkuNTA1ODZDMi45NTExNyA5LjYxNTIzIDMuMTAxNTYgOS42OTMzNiAzLjI2OTUzIDkuNzQwMjNDMy40Mzc1IDkuNzg3MTEgMy42MDkzOCA5LjgxMDU1IDMuNzg1MTYgOS44MTA1NUM0LjIwMzEyIDkuODEwNTUgNC41MTk1MyA5LjcxMjg5IDQuNzM0MzggOS41MTc1OEM0Ljk0OTIyIDkuMzIyMjcgNS4wNTY2NCA5LjA3MDMxIDUuMDU2NjQgOC43NjE3MlpNMTMuNDE4IDEyLjI3MTVIOC4wNzQyMlYxMUgxMy40MThWMTIuMjcxNVoiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDMuOTUyNjQgNikiIGZpbGw9IndoaXRlIi8+Cjwvc3ZnPgo=);
+  --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-toc: url(data:image/svg+xml;base64,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);
+  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-users: url(data:image/svg+xml;base64,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);
+  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-word: url(data:image/svg+xml;base64,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);
+  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
+}
+
+/* Icon CSS class declarations */
+
+.jp-AddAboveIcon {
+  background-image: var(--jp-icon-add-above);
+}
+
+.jp-AddBelowIcon {
+  background-image: var(--jp-icon-add-below);
+}
+
+.jp-AddIcon {
+  background-image: var(--jp-icon-add);
+}
+
+.jp-BellIcon {
+  background-image: var(--jp-icon-bell);
+}
+
+.jp-BugDotIcon {
+  background-image: var(--jp-icon-bug-dot);
+}
+
+.jp-BugIcon {
+  background-image: var(--jp-icon-bug);
+}
+
+.jp-BuildIcon {
+  background-image: var(--jp-icon-build);
+}
+
+.jp-CaretDownEmptyIcon {
+  background-image: var(--jp-icon-caret-down-empty);
+}
+
+.jp-CaretDownEmptyThinIcon {
+  background-image: var(--jp-icon-caret-down-empty-thin);
+}
+
+.jp-CaretDownIcon {
+  background-image: var(--jp-icon-caret-down);
+}
+
+.jp-CaretLeftIcon {
+  background-image: var(--jp-icon-caret-left);
+}
+
+.jp-CaretRightIcon {
+  background-image: var(--jp-icon-caret-right);
+}
+
+.jp-CaretUpEmptyThinIcon {
+  background-image: var(--jp-icon-caret-up-empty-thin);
+}
+
+.jp-CaretUpIcon {
+  background-image: var(--jp-icon-caret-up);
+}
+
+.jp-CaseSensitiveIcon {
+  background-image: var(--jp-icon-case-sensitive);
+}
+
+.jp-CheckIcon {
+  background-image: var(--jp-icon-check);
+}
+
+.jp-CircleEmptyIcon {
+  background-image: var(--jp-icon-circle-empty);
+}
+
+.jp-CircleIcon {
+  background-image: var(--jp-icon-circle);
+}
+
+.jp-ClearIcon {
+  background-image: var(--jp-icon-clear);
+}
+
+.jp-CloseIcon {
+  background-image: var(--jp-icon-close);
+}
+
+.jp-CodeCheckIcon {
+  background-image: var(--jp-icon-code-check);
+}
+
+.jp-CodeIcon {
+  background-image: var(--jp-icon-code);
+}
+
+.jp-CollapseAllIcon {
+  background-image: var(--jp-icon-collapse-all);
+}
+
+.jp-ConsoleIcon {
+  background-image: var(--jp-icon-console);
+}
+
+.jp-CopyIcon {
+  background-image: var(--jp-icon-copy);
+}
+
+.jp-CopyrightIcon {
+  background-image: var(--jp-icon-copyright);
+}
+
+.jp-CutIcon {
+  background-image: var(--jp-icon-cut);
+}
+
+.jp-DeleteIcon {
+  background-image: var(--jp-icon-delete);
+}
+
+.jp-DownloadIcon {
+  background-image: var(--jp-icon-download);
+}
+
+.jp-DuplicateIcon {
+  background-image: var(--jp-icon-duplicate);
+}
+
+.jp-EditIcon {
+  background-image: var(--jp-icon-edit);
+}
+
+.jp-EllipsesIcon {
+  background-image: var(--jp-icon-ellipses);
+}
+
+.jp-ErrorIcon {
+  background-image: var(--jp-icon-error);
+}
+
+.jp-ExpandAllIcon {
+  background-image: var(--jp-icon-expand-all);
+}
+
+.jp-ExtensionIcon {
+  background-image: var(--jp-icon-extension);
+}
+
+.jp-FastForwardIcon {
+  background-image: var(--jp-icon-fast-forward);
+}
+
+.jp-FileIcon {
+  background-image: var(--jp-icon-file);
+}
+
+.jp-FileUploadIcon {
+  background-image: var(--jp-icon-file-upload);
+}
+
+.jp-FilterDotIcon {
+  background-image: var(--jp-icon-filter-dot);
+}
+
+.jp-FilterIcon {
+  background-image: var(--jp-icon-filter);
+}
+
+.jp-FilterListIcon {
+  background-image: var(--jp-icon-filter-list);
+}
+
+.jp-FolderFavoriteIcon {
+  background-image: var(--jp-icon-folder-favorite);
+}
+
+.jp-FolderIcon {
+  background-image: var(--jp-icon-folder);
+}
+
+.jp-HomeIcon {
+  background-image: var(--jp-icon-home);
+}
+
+.jp-Html5Icon {
+  background-image: var(--jp-icon-html5);
+}
+
+.jp-ImageIcon {
+  background-image: var(--jp-icon-image);
+}
+
+.jp-InfoIcon {
+  background-image: var(--jp-icon-info);
+}
+
+.jp-InspectorIcon {
+  background-image: var(--jp-icon-inspector);
+}
+
+.jp-JsonIcon {
+  background-image: var(--jp-icon-json);
+}
+
+.jp-JuliaIcon {
+  background-image: var(--jp-icon-julia);
+}
+
+.jp-JupyterFaviconIcon {
+  background-image: var(--jp-icon-jupyter-favicon);
+}
+
+.jp-JupyterIcon {
+  background-image: var(--jp-icon-jupyter);
+}
+
+.jp-JupyterlabWordmarkIcon {
+  background-image: var(--jp-icon-jupyterlab-wordmark);
+}
+
+.jp-KernelIcon {
+  background-image: var(--jp-icon-kernel);
+}
+
+.jp-KeyboardIcon {
+  background-image: var(--jp-icon-keyboard);
+}
+
+.jp-LaunchIcon {
+  background-image: var(--jp-icon-launch);
+}
+
+.jp-LauncherIcon {
+  background-image: var(--jp-icon-launcher);
+}
+
+.jp-LineFormIcon {
+  background-image: var(--jp-icon-line-form);
+}
+
+.jp-LinkIcon {
+  background-image: var(--jp-icon-link);
+}
+
+.jp-ListIcon {
+  background-image: var(--jp-icon-list);
+}
+
+.jp-MarkdownIcon {
+  background-image: var(--jp-icon-markdown);
+}
+
+.jp-MoveDownIcon {
+  background-image: var(--jp-icon-move-down);
+}
+
+.jp-MoveUpIcon {
+  background-image: var(--jp-icon-move-up);
+}
+
+.jp-NewFolderIcon {
+  background-image: var(--jp-icon-new-folder);
+}
+
+.jp-NotTrustedIcon {
+  background-image: var(--jp-icon-not-trusted);
+}
+
+.jp-NotebookIcon {
+  background-image: var(--jp-icon-notebook);
+}
+
+.jp-NumberingIcon {
+  background-image: var(--jp-icon-numbering);
+}
+
+.jp-OfflineBoltIcon {
+  background-image: var(--jp-icon-offline-bolt);
+}
+
+.jp-PaletteIcon {
+  background-image: var(--jp-icon-palette);
+}
+
+.jp-PasteIcon {
+  background-image: var(--jp-icon-paste);
+}
+
+.jp-PdfIcon {
+  background-image: var(--jp-icon-pdf);
+}
+
+.jp-PythonIcon {
+  background-image: var(--jp-icon-python);
+}
+
+.jp-RKernelIcon {
+  background-image: var(--jp-icon-r-kernel);
+}
+
+.jp-ReactIcon {
+  background-image: var(--jp-icon-react);
+}
+
+.jp-RedoIcon {
+  background-image: var(--jp-icon-redo);
+}
+
+.jp-RefreshIcon {
+  background-image: var(--jp-icon-refresh);
+}
+
+.jp-RegexIcon {
+  background-image: var(--jp-icon-regex);
+}
+
+.jp-RunIcon {
+  background-image: var(--jp-icon-run);
+}
+
+.jp-RunningIcon {
+  background-image: var(--jp-icon-running);
+}
+
+.jp-SaveIcon {
+  background-image: var(--jp-icon-save);
+}
+
+.jp-SearchIcon {
+  background-image: var(--jp-icon-search);
+}
+
+.jp-SettingsIcon {
+  background-image: var(--jp-icon-settings);
+}
+
+.jp-ShareIcon {
+  background-image: var(--jp-icon-share);
+}
+
+.jp-SpreadsheetIcon {
+  background-image: var(--jp-icon-spreadsheet);
+}
+
+.jp-StopIcon {
+  background-image: var(--jp-icon-stop);
+}
+
+.jp-TabIcon {
+  background-image: var(--jp-icon-tab);
+}
+
+.jp-TableRowsIcon {
+  background-image: var(--jp-icon-table-rows);
+}
+
+.jp-TagIcon {
+  background-image: var(--jp-icon-tag);
+}
+
+.jp-TerminalIcon {
+  background-image: var(--jp-icon-terminal);
+}
+
+.jp-TextEditorIcon {
+  background-image: var(--jp-icon-text-editor);
+}
+
+.jp-TocIcon {
+  background-image: var(--jp-icon-toc);
+}
+
+.jp-TreeViewIcon {
+  background-image: var(--jp-icon-tree-view);
+}
+
+.jp-TrustedIcon {
+  background-image: var(--jp-icon-trusted);
+}
+
+.jp-UndoIcon {
+  background-image: var(--jp-icon-undo);
+}
+
+.jp-UserIcon {
+  background-image: var(--jp-icon-user);
+}
+
+.jp-UsersIcon {
+  background-image: var(--jp-icon-users);
+}
+
+.jp-VegaIcon {
+  background-image: var(--jp-icon-vega);
+}
+
+.jp-WordIcon {
+  background-image: var(--jp-icon-word);
+}
+
+.jp-YamlIcon {
+  background-image: var(--jp-icon-yaml);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+.jp-Icon,
+.jp-MaterialIcon {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-cover {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: cover;
+}
+
+/**
+ * (DEPRECATED) Support for specific CSS icon sizes
+ */
+
+.jp-Icon-16 {
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-18 {
+  background-size: 18px;
+  min-width: 18px;
+  min-height: 18px;
+}
+
+.jp-Icon-20 {
+  background-size: 20px;
+  min-width: 20px;
+  min-height: 20px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.lm-TabBar .lm-TabBar-addButton {
+  align-items: center;
+  display: flex;
+  padding: 4px;
+  padding-bottom: 5px;
+  margin-right: 1px;
+  background-color: var(--jp-layout-color2);
+}
+
+.lm-TabBar .lm-TabBar-addButton:hover {
+  background-color: var(--jp-layout-color1);
+}
+
+.lm-DockPanel-tabBar .lm-TabBar-tab {
+  width: var(--jp-private-horizontal-tab-width);
+}
+
+.lm-DockPanel-tabBar .lm-TabBar-content {
+  flex: unset;
+}
+
+.lm-DockPanel-tabBar[data-orientation='horizontal'] {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for icons as inline SVG HTMLElements
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-accent0[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-accent1[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-accent2[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-accent3[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-accent4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-accent0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-accent1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-accent2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-accent3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-accent4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-none[fill] {
+  fill: none;
+}
+
+.jp-icon-none[stroke] {
+  stroke: none;
+}
+
+/* brand icon colors. Same for light and dark */
+.jp-icon-brand0[fill] {
+  fill: var(--jp-brand-color0);
+}
+
+.jp-icon-brand1[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+.jp-icon-brand2[fill] {
+  fill: var(--jp-brand-color2);
+}
+
+.jp-icon-brand3[fill] {
+  fill: var(--jp-brand-color3);
+}
+
+.jp-icon-brand4[fill] {
+  fill: var(--jp-brand-color4);
+}
+
+.jp-icon-brand0[stroke] {
+  stroke: var(--jp-brand-color0);
+}
+
+.jp-icon-brand1[stroke] {
+  stroke: var(--jp-brand-color1);
+}
+
+.jp-icon-brand2[stroke] {
+  stroke: var(--jp-brand-color2);
+}
+
+.jp-icon-brand3[stroke] {
+  stroke: var(--jp-brand-color3);
+}
+
+.jp-icon-brand4[stroke] {
+  stroke: var(--jp-brand-color4);
+}
+
+/* warn icon colors. Same for light and dark */
+.jp-icon-warn0[fill] {
+  fill: var(--jp-warn-color0);
+}
+
+.jp-icon-warn1[fill] {
+  fill: var(--jp-warn-color1);
+}
+
+.jp-icon-warn2[fill] {
+  fill: var(--jp-warn-color2);
+}
+
+.jp-icon-warn3[fill] {
+  fill: var(--jp-warn-color3);
+}
+
+.jp-icon-warn0[stroke] {
+  stroke: var(--jp-warn-color0);
+}
+
+.jp-icon-warn1[stroke] {
+  stroke: var(--jp-warn-color1);
+}
+
+.jp-icon-warn2[stroke] {
+  stroke: var(--jp-warn-color2);
+}
+
+.jp-icon-warn3[stroke] {
+  stroke: var(--jp-warn-color3);
+}
+
+/* icon colors that contrast well with each other and most backgrounds */
+.jp-icon-contrast0[fill] {
+  fill: var(--jp-icon-contrast-color0);
+}
+
+.jp-icon-contrast1[fill] {
+  fill: var(--jp-icon-contrast-color1);
+}
+
+.jp-icon-contrast2[fill] {
+  fill: var(--jp-icon-contrast-color2);
+}
+
+.jp-icon-contrast3[fill] {
+  fill: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-contrast0[stroke] {
+  stroke: var(--jp-icon-contrast-color0);
+}
+
+.jp-icon-contrast1[stroke] {
+  stroke: var(--jp-icon-contrast-color1);
+}
+
+.jp-icon-contrast2[stroke] {
+  stroke: var(--jp-icon-contrast-color2);
+}
+
+.jp-icon-contrast3[stroke] {
+  stroke: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-dot[fill] {
+  fill: var(--jp-warn-color0);
+}
+
+.jp-jupyter-icon-color[fill] {
+  fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
+}
+
+.jp-notebook-icon-color[fill] {
+  fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
+}
+
+.jp-json-icon-color[fill] {
+  fill: var(--jp-json-icon-color, var(--jp-warn-color1));
+}
+
+.jp-console-icon-color[fill] {
+  fill: var(--jp-console-icon-color, white);
+}
+
+.jp-console-icon-background-color[fill] {
+  fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
+}
+
+.jp-terminal-icon-color[fill] {
+  fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
+}
+
+.jp-terminal-icon-background-color[fill] {
+  fill: var(
+    --jp-terminal-icon-background-color,
+    var(--jp-inverse-layout-color2)
+  );
+}
+
+.jp-text-editor-icon-color[fill] {
+  fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
+}
+
+.jp-inspector-icon-color[fill] {
+  fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
+}
+
+/* CSS for icons in selected filebrowser listing items */
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* stylelint-disable selector-max-class, selector-max-compound-selectors */
+
+/**
+* TODO: come up with non css-hack solution for showing the busy icon on top
+*  of the close icon
+* CSS for complex behavior of close icon of tabs in the main area tabbar
+*/
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
+}
+
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+/* stylelint-enable selector-max-class, selector-max-compound-selectors */
+
+/* CSS for icons in status bar */
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* special handling for splash icon CSS. While the theme CSS reloads during
+   splash, the splash icon can loose theming. To prevent that, we set a
+   default for its color variable */
+:root {
+  --jp-warn-color0: var(--md-orange-700);
+}
+
+/* not sure what to do with this one, used in filebrowser listing */
+.jp-DragIcon {
+  margin-right: 4px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for alt colors for icons as inline SVG HTMLElements
+ */
+
+/* alt recolor the primary elements of an icon */
+.jp-icon-alt .jp-icon0[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-alt .jp-icon1[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-alt .jp-icon2[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-alt .jp-icon3[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-alt .jp-icon4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-alt .jp-icon0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-alt .jp-icon1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-alt .jp-icon2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-alt .jp-icon3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-alt .jp-icon4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* alt recolor the accent elements of an icon */
+.jp-icon-alt .jp-icon-accent0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-alt .jp-icon-accent1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-alt .jp-icon-accent2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-alt .jp-icon-accent3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-alt .jp-icon-accent4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-alt .jp-icon-accent0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-alt .jp-icon-accent1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-alt .jp-icon-accent2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-alt .jp-icon-accent3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-alt .jp-icon-accent4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
+  display: none !important;
+}
+
+/**
+ * Support for hover colors for icons as inline SVG HTMLElements
+ */
+
+/**
+ * regular colors
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon-hover :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-hover :hover .jp-icon-none-hover[fill] {
+  fill: none;
+}
+
+.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
+  stroke: none;
+}
+
+/**
+ * inverse colors
+ */
+
+/* inverse recolor the primary elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* inverse recolor the accent elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-IFrame {
+  width: 100%;
+  height: 100%;
+}
+
+.jp-IFrame > iframe {
+  border: none;
+}
+
+/*
+When drag events occur, `lm-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-IFrame {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-IFrame::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-HoverBox {
+  position: fixed;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FormGroup-content fieldset {
+  border: none;
+  padding: 0;
+  min-width: 0;
+  width: 100%;
+}
+
+/* stylelint-disable selector-max-type */
+
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
+  font-size: var(--jp-content-font-size2);
+  border-color: var(--jp-input-border-color);
+  border-style: solid;
+  border-radius: var(--jp-border-radius);
+  border-width: 1px;
+  padding: 6px 8px;
+  background: none;
+  color: var(--jp-ui-font-color0);
+  height: inherit;
+}
+
+.jp-FormGroup-content fieldset input[type='checkbox'] {
+  position: relative;
+  top: 2px;
+  margin-left: 0;
+}
+
+.jp-FormGroup-content button.jp-mod-styled {
+  cursor: pointer;
+}
+
+.jp-FormGroup-content .checkbox label {
+  cursor: pointer;
+  font-size: var(--jp-content-font-size1);
+}
+
+.jp-FormGroup-content .jp-root > fieldset > legend {
+  display: none;
+}
+
+.jp-FormGroup-content .jp-root > fieldset > p {
+  display: none;
+}
+
+/** copy of `input.jp-mod-styled:focus` style */
+.jp-FormGroup-content fieldset input:focus,
+.jp-FormGroup-content fieldset select:focus {
+  -moz-outline-radius: unset;
+  outline: var(--jp-border-width) solid var(--md-blue-500);
+  outline-offset: -1px;
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-FormGroup-content fieldset input:hover:not(:focus),
+.jp-FormGroup-content fieldset select:hover:not(:focus) {
+  background-color: var(--jp-border-color2);
+}
+
+/* stylelint-enable selector-max-type */
+
+.jp-FormGroup-content .checkbox .field-description {
+  /* Disable default description field for checkbox:
+   because other widgets do not have description fields,
+   we add descriptions to each widget on the field level.
+  */
+  display: none;
+}
+
+.jp-FormGroup-content #root__description {
+  display: none;
+}
+
+.jp-FormGroup-content .jp-modifiedIndicator {
+  width: 5px;
+  background-color: var(--jp-brand-color2);
+  margin-top: 0;
+  margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
+  flex-shrink: 0;
+}
+
+.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
+  background-color: var(--jp-error-color0);
+  margin-right: 0.5em;
+}
+
+/* RJSF ARRAY style */
+
+.jp-arrayFieldWrapper legend {
+  font-size: var(--jp-content-font-size2);
+  color: var(--jp-ui-font-color0);
+  flex-basis: 100%;
+  padding: 4px 0;
+  font-weight: var(--jp-content-heading-font-weight);
+  border-bottom: 1px solid var(--jp-border-color2);
+}
+
+.jp-arrayFieldWrapper .field-description {
+  padding: 4px 0;
+  white-space: pre-wrap;
+}
+
+.jp-arrayFieldWrapper .array-item {
+  width: 100%;
+  border: 1px solid var(--jp-border-color2);
+  border-radius: 4px;
+  margin: 4px;
+}
+
+.jp-ArrayOperations {
+  display: flex;
+  margin-left: 8px;
+}
+
+.jp-ArrayOperationsButton {
+  margin: 2px;
+}
+
+.jp-ArrayOperationsButton .jp-icon3[fill] {
+  fill: var(--jp-ui-font-color0);
+}
+
+button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
+  cursor: not-allowed;
+  opacity: 0.5;
+}
+
+/* RJSF form validation error */
+
+.jp-FormGroup-content .validationErrors {
+  color: var(--jp-error-color0);
+}
+
+/* Hide panel level error as duplicated the field level error */
+.jp-FormGroup-content .panel.errors {
+  display: none;
+}
+
+/* RJSF normal content (settings-editor) */
+
+.jp-FormGroup-contentNormal {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
+  margin-left: 7px;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-description {
+  flex-basis: 100%;
+  padding: 4px 7px;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-default {
+  flex-basis: 100%;
+  padding: 4px 7px;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
+  font-size: var(--jp-content-font-size1);
+  font-weight: normal;
+  min-width: 120px;
+}
+
+.jp-FormGroup-contentNormal fieldset:not(:first-child) {
+  margin-left: 7px;
+}
+
+.jp-FormGroup-contentNormal .field-array-of-string .array-item {
+  /* Display `jp-ArrayOperations` buttons side-by-side with content except
+    for small screens where flex-wrap will place them one below the other.
+  */
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+}
+
+.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
+  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+  margin-top: 2px;
+}
+
+/* RJSF compact content (metadata-form) */
+
+.jp-FormGroup-content.jp-FormGroup-contentCompact {
+  width: 100%;
+}
+
+.jp-FormGroup-contentCompact .form-group {
+  display: flex;
+  padding: 0.5em 0.2em 0.5em 0;
+}
+
+.jp-FormGroup-contentCompact
+  .jp-FormGroup-compactTitle
+  .jp-FormGroup-description {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
+  padding-bottom: 0.3em;
+}
+
+.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
+  padding-bottom: 7px;
+}
+
+.jp-FormGroup-contentCompact
+  .jp-objectFieldWrapper
+  .jp-objectFieldWrapper
+  .form-group {
+  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+  margin-top: 2px;
+}
+
+.jp-FormGroup-contentCompact ul.error-detail {
+  margin-block-start: 0.5em;
+  margin-block-end: 0.5em;
+  padding-inline-start: 1em;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-SidePanel {
+  display: flex;
+  flex-direction: column;
+  min-width: var(--jp-sidebar-min-width);
+  overflow-y: auto;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  font-size: var(--jp-ui-font-size1);
+}
+
+.jp-SidePanel-header {
+  flex: 0 0 auto;
+  display: flex;
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin: 0;
+  padding: 2px;
+  text-transform: uppercase;
+}
+
+.jp-SidePanel-toolbar {
+  flex: 0 0 auto;
+}
+
+.jp-SidePanel-content {
+  flex: 1 1 auto;
+}
+
+.jp-SidePanel-toolbar,
+.jp-AccordionPanel-toolbar {
+  height: var(--jp-private-toolbar-height);
+}
+
+.jp-SidePanel-toolbar.jp-Toolbar-micro {
+  display: none;
+}
+
+.lm-AccordionPanel .jp-AccordionPanel-title {
+  box-sizing: border-box;
+  line-height: 25px;
+  margin: 0;
+  display: flex;
+  align-items: center;
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  font-size: var(--jp-ui-font-size0);
+}
+
+.jp-AccordionPanel-title {
+  cursor: pointer;
+  user-select: none;
+  -moz-user-select: none;
+  -webkit-user-select: none;
+  text-transform: uppercase;
+}
+
+.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
+  /* Title is rotated for horizontal accordion panel using CSS */
+  display: block;
+  transform-origin: top left;
+  transform: rotate(-90deg) translate(-100%);
+}
+
+.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
+  user-select: none;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  overflow: hidden;
+}
+
+.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
+  transform: rotate(-90deg);
+  margin: auto 0;
+  height: 16px;
+}
+
+.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
+  transform: rotate(0deg);
+}
+
+.lm-AccordionPanel .jp-AccordionPanel-toolbar {
+  background: none;
+  box-shadow: none;
+  border: none;
+  margin-left: auto;
+}
+
+.lm-AccordionPanel .lm-SplitPanel-handle:hover {
+  background: var(--jp-layout-color3);
+}
+
+.jp-text-truncated {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Spinner {
+  position: absolute;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  z-index: 10;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-layout-color0);
+  outline: none;
+}
+
+.jp-SpinnerContent {
+  font-size: 10px;
+  margin: 50px auto;
+  text-indent: -9999em;
+  width: 3em;
+  height: 3em;
+  border-radius: 50%;
+  background: var(--jp-brand-color3);
+  background: linear-gradient(
+    to right,
+    #f37626 10%,
+    rgba(255, 255, 255, 0) 42%
+  );
+  position: relative;
+  animation: load3 1s infinite linear, fadeIn 1s;
+}
+
+.jp-SpinnerContent::before {
+  width: 50%;
+  height: 50%;
+  background: #f37626;
+  border-radius: 100% 0 0;
+  position: absolute;
+  top: 0;
+  left: 0;
+  content: '';
+}
+
+.jp-SpinnerContent::after {
+  background: var(--jp-layout-color0);
+  width: 75%;
+  height: 75%;
+  border-radius: 50%;
+  content: '';
+  margin: auto;
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  right: 0;
+}
+
+@keyframes fadeIn {
+  0% {
+    opacity: 0;
+  }
+
+  100% {
+    opacity: 1;
+  }
+}
+
+@keyframes load3 {
+  0% {
+    transform: rotate(0deg);
+  }
+
+  100% {
+    transform: rotate(360deg);
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+button.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: none;
+  box-sizing: border-box;
+  text-align: center;
+  line-height: 32px;
+  height: 32px;
+  padding: 0 12px;
+  letter-spacing: 0.8px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input.jp-mod-styled {
+  background: var(--jp-input-background);
+  height: 28px;
+  box-sizing: border-box;
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+  padding-left: 7px;
+  padding-right: 7px;
+  font-size: var(--jp-ui-font-size2);
+  color: var(--jp-ui-font-color0);
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input[type='checkbox'].jp-mod-styled {
+  appearance: checkbox;
+  -webkit-appearance: checkbox;
+  -moz-appearance: checkbox;
+  height: auto;
+}
+
+input.jp-mod-styled:focus {
+  border: var(--jp-border-width) solid var(--md-blue-500);
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-select-wrapper {
+  display: flex;
+  position: relative;
+  flex-direction: column;
+  padding: 1px;
+  background-color: var(--jp-layout-color1);
+  box-sizing: border-box;
+  margin-bottom: 12px;
+}
+
+.jp-select-wrapper:not(.multiple) {
+  height: 28px;
+}
+
+.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-input-active-background);
+}
+
+select.jp-mod-styled:hover {
+  cursor: pointer;
+  color: var(--jp-ui-font-color0);
+  background-color: var(--jp-input-hover-background);
+  box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
+}
+
+select.jp-mod-styled {
+  flex: 1 1 auto;
+  width: 100%;
+  font-size: var(--jp-ui-font-size2);
+  background: var(--jp-input-background);
+  color: var(--jp-ui-font-color0);
+  padding: 0 25px 0 8px;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+select.jp-mod-styled:not([multiple]) {
+  height: 32px;
+}
+
+select.jp-mod-styled[multiple] {
+  max-height: 200px;
+  overflow-y: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-switch {
+  display: flex;
+  align-items: center;
+  padding-left: 4px;
+  padding-right: 4px;
+  font-size: var(--jp-ui-font-size1);
+  background-color: transparent;
+  color: var(--jp-ui-font-color1);
+  border: none;
+  height: 20px;
+}
+
+.jp-switch:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-switch-label {
+  margin-right: 5px;
+  font-family: var(--jp-ui-font-family);
+}
+
+.jp-switch-track {
+  cursor: pointer;
+  background-color: var(--jp-switch-color, var(--jp-border-color1));
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 34px;
+  height: 16px;
+  width: 35px;
+  position: relative;
+}
+
+.jp-switch-track::before {
+  content: '';
+  position: absolute;
+  height: 10px;
+  width: 10px;
+  margin: 3px;
+  left: 0;
+  background-color: var(--jp-ui-inverse-font-color1);
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 50%;
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track {
+  background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track::before {
+  /* track width (35) - margins (3 + 3) - thumb width (10) */
+  left: 19px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toolbar-height: calc(
+    28px + var(--jp-border-width)
+  ); /* leave 28px for content */
+}
+
+.jp-Toolbar {
+  color: var(--jp-ui-font-color1);
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  background: var(--jp-toolbar-background);
+  min-height: var(--jp-toolbar-micro-height);
+  padding: 2px;
+  z-index: 8;
+  overflow-x: hidden;
+}
+
+/* Toolbar items */
+
+.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
+  flex-grow: 1;
+  flex-shrink: 1;
+}
+
+.jp-Toolbar-item.jp-Toolbar-kernelStatus {
+  display: inline-block;
+  width: 32px;
+  background-repeat: no-repeat;
+  background-position: center;
+  background-size: 16px;
+}
+
+.jp-Toolbar > .jp-Toolbar-item {
+  flex: 0 0 auto;
+  display: flex;
+  padding-left: 1px;
+  padding-right: 1px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: var(--jp-private-toolbar-height);
+  height: 100%;
+}
+
+/* Toolbar buttons */
+
+/* This is the div we use to wrap the react component into a Widget */
+div.jp-ToolbarButton {
+  color: transparent;
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0;
+  margin: 0;
+}
+
+button.jp-ToolbarButtonComponent {
+  background: var(--jp-layout-color1);
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0 6px;
+  margin: 0;
+  height: 24px;
+  border-radius: var(--jp-border-radius);
+  display: flex;
+  align-items: center;
+  text-align: center;
+  font-size: 14px;
+  min-width: unset;
+  min-height: unset;
+}
+
+button.jp-ToolbarButtonComponent:disabled {
+  opacity: 0.4;
+}
+
+button.jp-ToolbarButtonComponent > span {
+  padding: 0;
+  flex: 0 0 auto;
+}
+
+button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
+  font-size: var(--jp-ui-font-size1);
+  line-height: 100%;
+  padding-left: 2px;
+  color: var(--jp-ui-font-color1);
+  font-family: var(--jp-ui-font-family);
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar.jp-Toolbar-micro {
+  padding: 0;
+  min-height: 0;
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar {
+  border: none;
+  box-shadow: none;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-WindowedPanel-outer {
+  position: relative;
+  overflow-y: auto;
+}
+
+.jp-WindowedPanel-inner {
+  position: relative;
+}
+
+.jp-WindowedPanel-window {
+  position: absolute;
+  left: 0;
+  right: 0;
+  overflow: visible;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* Sibling imports */
+
+body {
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+}
+
+/* Disable native link decoration styles everywhere outside of dialog boxes */
+a {
+  text-decoration: unset;
+  color: unset;
+}
+
+a:hover {
+  text-decoration: unset;
+  color: unset;
+}
+
+/* Accessibility for links inside dialog box text */
+.jp-Dialog-content a {
+  text-decoration: revert;
+  color: var(--jp-content-link-color);
+}
+
+.jp-Dialog-content a:hover {
+  text-decoration: revert;
+}
+
+/* Styles for ui-components */
+.jp-Button {
+  color: var(--jp-ui-font-color2);
+  border-radius: var(--jp-border-radius);
+  padding: 0 12px;
+  font-size: var(--jp-ui-font-size1);
+
+  /* Copy from blueprint 3 */
+  display: inline-flex;
+  flex-direction: row;
+  border: none;
+  cursor: pointer;
+  align-items: center;
+  justify-content: center;
+  text-align: left;
+  vertical-align: middle;
+  min-height: 30px;
+  min-width: 30px;
+}
+
+.jp-Button:disabled {
+  cursor: not-allowed;
+}
+
+.jp-Button:empty {
+  padding: 0 !important;
+}
+
+.jp-Button.jp-mod-small {
+  min-height: 24px;
+  min-width: 24px;
+  font-size: 12px;
+  padding: 0 7px;
+}
+
+/* Use our own theme for hover styles */
+.jp-Button.jp-mod-minimal:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-Button.jp-mod-minimal {
+  background: none;
+}
+
+.jp-InputGroup {
+  display: block;
+  position: relative;
+}
+
+.jp-InputGroup input {
+  box-sizing: border-box;
+  border: none;
+  border-radius: 0;
+  background-color: transparent;
+  color: var(--jp-ui-font-color0);
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+  padding-bottom: 0;
+  padding-top: 0;
+  padding-left: 10px;
+  padding-right: 28px;
+  position: relative;
+  width: 100%;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+  font-size: 14px;
+  font-weight: 400;
+  height: 30px;
+  line-height: 30px;
+  outline: none;
+  vertical-align: middle;
+}
+
+.jp-InputGroup input:focus {
+  box-shadow: inset 0 0 0 var(--jp-border-width)
+      var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-InputGroup input:disabled {
+  cursor: not-allowed;
+  resize: block;
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroup input:disabled ~ span {
+  cursor: not-allowed;
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroup input::placeholder,
+input::placeholder {
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroupAction {
+  position: absolute;
+  bottom: 1px;
+  right: 0;
+  padding: 6px;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select {
+  background-color: initial;
+  border: none;
+  border-radius: 0;
+  box-shadow: none;
+  color: var(--jp-ui-font-color0);
+  display: block;
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  height: 24px;
+  line-height: 14px;
+  padding: 0 25px 0 10px;
+  text-align: left;
+  -moz-appearance: none;
+  -webkit-appearance: none;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color2);
+  cursor: not-allowed;
+  resize: block;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
+  cursor: not-allowed;
+}
+
+/* Use our own theme for hover and option styles */
+/* stylelint-disable-next-line selector-max-type */
+.jp-HTMLSelect.jp-DefaultStyle select:hover,
+.jp-HTMLSelect.jp-DefaultStyle select > option {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color0);
+}
+
+select {
+  box-sizing: border-box;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-StatusBar-Widget {
+  display: flex;
+  align-items: center;
+  background: var(--jp-layout-color2);
+  min-height: var(--jp-statusbar-height);
+  justify-content: space-between;
+  padding: 0 10px;
+}
+
+.jp-StatusBar-Left {
+  display: flex;
+  align-items: center;
+  flex-direction: row;
+}
+
+.jp-StatusBar-Middle {
+  display: flex;
+  align-items: center;
+}
+
+.jp-StatusBar-Right {
+  display: flex;
+  align-items: center;
+  flex-direction: row-reverse;
+}
+
+.jp-StatusBar-Item {
+  max-height: var(--jp-statusbar-height);
+  margin: 0 2px;
+  height: var(--jp-statusbar-height);
+  white-space: nowrap;
+  text-overflow: ellipsis;
+  color: var(--jp-ui-font-color1);
+  padding: 0 6px;
+}
+
+.jp-mod-highlighted:hover {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-mod-clicked {
+  background-color: var(--jp-brand-color1);
+}
+
+.jp-mod-clicked:hover {
+  background-color: var(--jp-brand-color0);
+}
+
+.jp-mod-clicked .jp-StatusBar-TextItem {
+  color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-StatusBar-HoverItem {
+  box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
+}
+
+.jp-StatusBar-TextItem {
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  line-height: 24px;
+  color: var(--jp-ui-font-color1);
+}
+
+.jp-StatusBar-GroupItem {
+  display: flex;
+  align-items: center;
+  flex-direction: row;
+}
+
+.jp-Statusbar-ProgressCircle svg {
+  display: block;
+  margin: 0 auto;
+  width: 16px;
+  height: 24px;
+  align-self: normal;
+}
+
+.jp-Statusbar-ProgressCircle path {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-Statusbar-ProgressBar-progress-bar {
+  height: 10px;
+  width: 100px;
+  border: solid 0.25px var(--jp-brand-color2);
+  border-radius: 3px;
+  overflow: hidden;
+  align-self: center;
+}
+
+.jp-Statusbar-ProgressBar-progress-bar > div {
+  background-color: var(--jp-brand-color2);
+  background-image: linear-gradient(
+    -45deg,
+    rgba(255, 255, 255, 0.2) 25%,
+    transparent 25%,
+    transparent 50%,
+    rgba(255, 255, 255, 0.2) 50%,
+    rgba(255, 255, 255, 0.2) 75%,
+    transparent 75%,
+    transparent
+  );
+  background-size: 40px 40px;
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 14px;
+  color: #fff;
+  text-align: center;
+  animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
+}
+
+.jp-Statusbar-ProgressBar-progress-bar p {
+  color: var(--jp-ui-font-color1);
+  font-family: var(--jp-ui-font-family);
+  font-size: var(--jp-ui-font-size1);
+  line-height: 10px;
+  width: 100px;
+}
+
+@keyframes jp-Statusbar-ExecutionTime-progress-bar {
+  0% {
+    background-position: 0 0;
+  }
+
+  100% {
+    background-position: 40px 40px;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-commandpalette-search-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Overall styles
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  padding-bottom: 0;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Modal variant
+|----------------------------------------------------------------------------*/
+
+.jp-ModalCommandPalette {
+  position: absolute;
+  z-index: 10000;
+  top: 38px;
+  left: 30%;
+  margin: 0;
+  padding: 4px;
+  width: 40%;
+  box-shadow: var(--jp-elevation-z4);
+  border-radius: 4px;
+  background: var(--jp-layout-color0);
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette {
+  max-height: 40vh;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
+  margin-left: 4px;
+  margin-right: 4px;
+}
+
+.jp-ModalCommandPalette
+  .lm-CommandPalette
+  .lm-CommandPalette-item.lm-mod-disabled {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Search
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-search {
+  padding: 4px;
+  background-color: var(--jp-layout-color1);
+  z-index: 2;
+}
+
+.lm-CommandPalette-wrapper {
+  overflow: overlay;
+  padding: 0 9px;
+  background-color: var(--jp-input-active-background);
+  height: 30px;
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+}
+
+.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
+  box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-SearchIconGroup {
+  color: white;
+  background-color: var(--jp-brand-color1);
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  padding: 5px 5px 1px;
+}
+
+.jp-SearchIconGroup svg {
+  height: 20px;
+  width: 20px;
+}
+
+.jp-SearchIconGroup .jp-icon3[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-input {
+  background: transparent;
+  width: calc(100% - 18px);
+  float: left;
+  border: none;
+  outline: none;
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  line-height: var(--jp-private-commandpalette-search-height);
+}
+
+.lm-CommandPalette-input::-webkit-input-placeholder,
+.lm-CommandPalette-input::-moz-placeholder,
+.lm-CommandPalette-input:-ms-input-placeholder {
+  color: var(--jp-ui-font-color2);
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Results
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-header:first-child {
+  margin-top: 0;
+}
+
+.lm-CommandPalette-header {
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin-top: 8px;
+  padding: 8px 0 8px 12px;
+  text-transform: uppercase;
+}
+
+.lm-CommandPalette-header.lm-mod-active {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-header > mark {
+  background-color: transparent;
+  font-weight: bold;
+  color: var(--jp-ui-font-color1);
+}
+
+.lm-CommandPalette-item {
+  padding: 4px 12px 4px 4px;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  font-weight: 400;
+  display: flex;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item.lm-mod-active {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-itemContent {
+  overflow: hidden;
+}
+
+.lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled mark {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
+  margin: 0 4px 0 0;
+  position: relative;
+  width: 16px;
+  top: 2px;
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
+  opacity: 0.6;
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemCaption {
+  display: none;
+}
+
+.lm-CommandPalette-content {
+  background-color: var(--jp-layout-color1);
+}
+
+.lm-CommandPalette-content:empty::after {
+  content: 'No results';
+  margin: auto;
+  margin-top: 20px;
+  width: 100px;
+  display: block;
+  font-size: var(--jp-ui-font-size2);
+  font-family: var(--jp-ui-font-family);
+  font-weight: lighter;
+}
+
+.lm-CommandPalette-emptyMessage {
+  text-align: center;
+  margin-top: 24px;
+  line-height: 1.32;
+  padding: 0 8px;
+  color: var(--jp-content-font-color3);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Dialog {
+  position: absolute;
+  z-index: 10000;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  top: 0;
+  left: 0;
+  margin: 0;
+  padding: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-dialog-background);
+}
+
+.jp-Dialog-content {
+  display: flex;
+  flex-direction: column;
+  margin-left: auto;
+  margin-right: auto;
+  background: var(--jp-layout-color1);
+  padding: 24px 24px 12px;
+  min-width: 300px;
+  min-height: 150px;
+  max-width: 1000px;
+  max-height: 500px;
+  box-sizing: border-box;
+  box-shadow: var(--jp-elevation-z20);
+  word-wrap: break-word;
+  border-radius: var(--jp-border-radius);
+
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color1);
+  resize: both;
+}
+
+.jp-Dialog-content.jp-Dialog-content-small {
+  max-width: 500px;
+}
+
+.jp-Dialog-button {
+  overflow: visible;
+}
+
+button.jp-Dialog-button:focus {
+  outline: 1px solid var(--jp-brand-color1);
+  outline-offset: 4px;
+  -moz-outline-radius: 0;
+}
+
+button.jp-Dialog-button:focus::-moz-focus-inner {
+  border: 0;
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+  outline-offset: 4px;
+  -moz-outline-radius: 0;
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
+  outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
+  outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+  outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
+}
+
+button.jp-Dialog-close-button {
+  padding: 0;
+  height: 100%;
+  min-width: unset;
+  min-height: unset;
+}
+
+.jp-Dialog-header {
+  display: flex;
+  justify-content: space-between;
+  flex: 0 0 auto;
+  padding-bottom: 12px;
+  font-size: var(--jp-ui-font-size3);
+  font-weight: 400;
+  color: var(--jp-ui-font-color1);
+}
+
+.jp-Dialog-body {
+  display: flex;
+  flex-direction: column;
+  flex: 1 1 auto;
+  font-size: var(--jp-ui-font-size1);
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
+}
+
+.jp-Dialog-footer {
+  display: flex;
+  flex-direction: row;
+  justify-content: flex-end;
+  align-items: center;
+  flex: 0 0 auto;
+  margin-left: -12px;
+  margin-right: -12px;
+  padding: 12px;
+}
+
+.jp-Dialog-checkbox {
+  padding-right: 5px;
+}
+
+.jp-Dialog-checkbox > input:focus-visible {
+  outline: 1px solid var(--jp-input-active-border-color);
+  outline-offset: 1px;
+}
+
+.jp-Dialog-spacer {
+  flex: 1 1 auto;
+}
+
+.jp-Dialog-title {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.jp-Dialog-body > .jp-select-wrapper {
+  width: 100%;
+}
+
+.jp-Dialog-body > button {
+  padding: 0 16px;
+}
+
+.jp-Dialog-body > label {
+  line-height: 1.4;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-Dialog-button.jp-mod-styled:not(:last-child) {
+  margin-right: 12px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Input-Boolean-Dialog {
+  flex-direction: row-reverse;
+  align-items: end;
+  width: 100%;
+}
+
+.jp-Input-Boolean-Dialog > label {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MainAreaWidget > :focus {
+  outline: none;
+}
+
+.jp-MainAreaWidget .jp-MainAreaWidget-error {
+  padding: 6px;
+}
+
+.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
+  width: auto;
+  padding: 10px;
+  background: var(--jp-error-color3);
+  border: var(--jp-border-width) solid var(--jp-error-color1);
+  border-radius: var(--jp-border-radius);
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  white-space: pre-wrap;
+  word-wrap: break-word;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/**
+ * google-material-color v1.2.6
+ * https://github.com/danlevan/google-material-color
+ */
+:root {
+  --md-red-50: #ffebee;
+  --md-red-100: #ffcdd2;
+  --md-red-200: #ef9a9a;
+  --md-red-300: #e57373;
+  --md-red-400: #ef5350;
+  --md-red-500: #f44336;
+  --md-red-600: #e53935;
+  --md-red-700: #d32f2f;
+  --md-red-800: #c62828;
+  --md-red-900: #b71c1c;
+  --md-red-A100: #ff8a80;
+  --md-red-A200: #ff5252;
+  --md-red-A400: #ff1744;
+  --md-red-A700: #d50000;
+  --md-pink-50: #fce4ec;
+  --md-pink-100: #f8bbd0;
+  --md-pink-200: #f48fb1;
+  --md-pink-300: #f06292;
+  --md-pink-400: #ec407a;
+  --md-pink-500: #e91e63;
+  --md-pink-600: #d81b60;
+  --md-pink-700: #c2185b;
+  --md-pink-800: #ad1457;
+  --md-pink-900: #880e4f;
+  --md-pink-A100: #ff80ab;
+  --md-pink-A200: #ff4081;
+  --md-pink-A400: #f50057;
+  --md-pink-A700: #c51162;
+  --md-purple-50: #f3e5f5;
+  --md-purple-100: #e1bee7;
+  --md-purple-200: #ce93d8;
+  --md-purple-300: #ba68c8;
+  --md-purple-400: #ab47bc;
+  --md-purple-500: #9c27b0;
+  --md-purple-600: #8e24aa;
+  --md-purple-700: #7b1fa2;
+  --md-purple-800: #6a1b9a;
+  --md-purple-900: #4a148c;
+  --md-purple-A100: #ea80fc;
+  --md-purple-A200: #e040fb;
+  --md-purple-A400: #d500f9;
+  --md-purple-A700: #a0f;
+  --md-deep-purple-50: #ede7f6;
+  --md-deep-purple-100: #d1c4e9;
+  --md-deep-purple-200: #b39ddb;
+  --md-deep-purple-300: #9575cd;
+  --md-deep-purple-400: #7e57c2;
+  --md-deep-purple-500: #673ab7;
+  --md-deep-purple-600: #5e35b1;
+  --md-deep-purple-700: #512da8;
+  --md-deep-purple-800: #4527a0;
+  --md-deep-purple-900: #311b92;
+  --md-deep-purple-A100: #b388ff;
+  --md-deep-purple-A200: #7c4dff;
+  --md-deep-purple-A400: #651fff;
+  --md-deep-purple-A700: #6200ea;
+  --md-indigo-50: #e8eaf6;
+  --md-indigo-100: #c5cae9;
+  --md-indigo-200: #9fa8da;
+  --md-indigo-300: #7986cb;
+  --md-indigo-400: #5c6bc0;
+  --md-indigo-500: #3f51b5;
+  --md-indigo-600: #3949ab;
+  --md-indigo-700: #303f9f;
+  --md-indigo-800: #283593;
+  --md-indigo-900: #1a237e;
+  --md-indigo-A100: #8c9eff;
+  --md-indigo-A200: #536dfe;
+  --md-indigo-A400: #3d5afe;
+  --md-indigo-A700: #304ffe;
+  --md-blue-50: #e3f2fd;
+  --md-blue-100: #bbdefb;
+  --md-blue-200: #90caf9;
+  --md-blue-300: #64b5f6;
+  --md-blue-400: #42a5f5;
+  --md-blue-500: #2196f3;
+  --md-blue-600: #1e88e5;
+  --md-blue-700: #1976d2;
+  --md-blue-800: #1565c0;
+  --md-blue-900: #0d47a1;
+  --md-blue-A100: #82b1ff;
+  --md-blue-A200: #448aff;
+  --md-blue-A400: #2979ff;
+  --md-blue-A700: #2962ff;
+  --md-light-blue-50: #e1f5fe;
+  --md-light-blue-100: #b3e5fc;
+  --md-light-blue-200: #81d4fa;
+  --md-light-blue-300: #4fc3f7;
+  --md-light-blue-400: #29b6f6;
+  --md-light-blue-500: #03a9f4;
+  --md-light-blue-600: #039be5;
+  --md-light-blue-700: #0288d1;
+  --md-light-blue-800: #0277bd;
+  --md-light-blue-900: #01579b;
+  --md-light-blue-A100: #80d8ff;
+  --md-light-blue-A200: #40c4ff;
+  --md-light-blue-A400: #00b0ff;
+  --md-light-blue-A700: #0091ea;
+  --md-cyan-50: #e0f7fa;
+  --md-cyan-100: #b2ebf2;
+  --md-cyan-200: #80deea;
+  --md-cyan-300: #4dd0e1;
+  --md-cyan-400: #26c6da;
+  --md-cyan-500: #00bcd4;
+  --md-cyan-600: #00acc1;
+  --md-cyan-700: #0097a7;
+  --md-cyan-800: #00838f;
+  --md-cyan-900: #006064;
+  --md-cyan-A100: #84ffff;
+  --md-cyan-A200: #18ffff;
+  --md-cyan-A400: #00e5ff;
+  --md-cyan-A700: #00b8d4;
+  --md-teal-50: #e0f2f1;
+  --md-teal-100: #b2dfdb;
+  --md-teal-200: #80cbc4;
+  --md-teal-300: #4db6ac;
+  --md-teal-400: #26a69a;
+  --md-teal-500: #009688;
+  --md-teal-600: #00897b;
+  --md-teal-700: #00796b;
+  --md-teal-800: #00695c;
+  --md-teal-900: #004d40;
+  --md-teal-A100: #a7ffeb;
+  --md-teal-A200: #64ffda;
+  --md-teal-A400: #1de9b6;
+  --md-teal-A700: #00bfa5;
+  --md-green-50: #e8f5e9;
+  --md-green-100: #c8e6c9;
+  --md-green-200: #a5d6a7;
+  --md-green-300: #81c784;
+  --md-green-400: #66bb6a;
+  --md-green-500: #4caf50;
+  --md-green-600: #43a047;
+  --md-green-700: #388e3c;
+  --md-green-800: #2e7d32;
+  --md-green-900: #1b5e20;
+  --md-green-A100: #b9f6ca;
+  --md-green-A200: #69f0ae;
+  --md-green-A400: #00e676;
+  --md-green-A700: #00c853;
+  --md-light-green-50: #f1f8e9;
+  --md-light-green-100: #dcedc8;
+  --md-light-green-200: #c5e1a5;
+  --md-light-green-300: #aed581;
+  --md-light-green-400: #9ccc65;
+  --md-light-green-500: #8bc34a;
+  --md-light-green-600: #7cb342;
+  --md-light-green-700: #689f38;
+  --md-light-green-800: #558b2f;
+  --md-light-green-900: #33691e;
+  --md-light-green-A100: #ccff90;
+  --md-light-green-A200: #b2ff59;
+  --md-light-green-A400: #76ff03;
+  --md-light-green-A700: #64dd17;
+  --md-lime-50: #f9fbe7;
+  --md-lime-100: #f0f4c3;
+  --md-lime-200: #e6ee9c;
+  --md-lime-300: #dce775;
+  --md-lime-400: #d4e157;
+  --md-lime-500: #cddc39;
+  --md-lime-600: #c0ca33;
+  --md-lime-700: #afb42b;
+  --md-lime-800: #9e9d24;
+  --md-lime-900: #827717;
+  --md-lime-A100: #f4ff81;
+  --md-lime-A200: #eeff41;
+  --md-lime-A400: #c6ff00;
+  --md-lime-A700: #aeea00;
+  --md-yellow-50: #fffde7;
+  --md-yellow-100: #fff9c4;
+  --md-yellow-200: #fff59d;
+  --md-yellow-300: #fff176;
+  --md-yellow-400: #ffee58;
+  --md-yellow-500: #ffeb3b;
+  --md-yellow-600: #fdd835;
+  --md-yellow-700: #fbc02d;
+  --md-yellow-800: #f9a825;
+  --md-yellow-900: #f57f17;
+  --md-yellow-A100: #ffff8d;
+  --md-yellow-A200: #ff0;
+  --md-yellow-A400: #ffea00;
+  --md-yellow-A700: #ffd600;
+  --md-amber-50: #fff8e1;
+  --md-amber-100: #ffecb3;
+  --md-amber-200: #ffe082;
+  --md-amber-300: #ffd54f;
+  --md-amber-400: #ffca28;
+  --md-amber-500: #ffc107;
+  --md-amber-600: #ffb300;
+  --md-amber-700: #ffa000;
+  --md-amber-800: #ff8f00;
+  --md-amber-900: #ff6f00;
+  --md-amber-A100: #ffe57f;
+  --md-amber-A200: #ffd740;
+  --md-amber-A400: #ffc400;
+  --md-amber-A700: #ffab00;
+  --md-orange-50: #fff3e0;
+  --md-orange-100: #ffe0b2;
+  --md-orange-200: #ffcc80;
+  --md-orange-300: #ffb74d;
+  --md-orange-400: #ffa726;
+  --md-orange-500: #ff9800;
+  --md-orange-600: #fb8c00;
+  --md-orange-700: #f57c00;
+  --md-orange-800: #ef6c00;
+  --md-orange-900: #e65100;
+  --md-orange-A100: #ffd180;
+  --md-orange-A200: #ffab40;
+  --md-orange-A400: #ff9100;
+  --md-orange-A700: #ff6d00;
+  --md-deep-orange-50: #fbe9e7;
+  --md-deep-orange-100: #ffccbc;
+  --md-deep-orange-200: #ffab91;
+  --md-deep-orange-300: #ff8a65;
+  --md-deep-orange-400: #ff7043;
+  --md-deep-orange-500: #ff5722;
+  --md-deep-orange-600: #f4511e;
+  --md-deep-orange-700: #e64a19;
+  --md-deep-orange-800: #d84315;
+  --md-deep-orange-900: #bf360c;
+  --md-deep-orange-A100: #ff9e80;
+  --md-deep-orange-A200: #ff6e40;
+  --md-deep-orange-A400: #ff3d00;
+  --md-deep-orange-A700: #dd2c00;
+  --md-brown-50: #efebe9;
+  --md-brown-100: #d7ccc8;
+  --md-brown-200: #bcaaa4;
+  --md-brown-300: #a1887f;
+  --md-brown-400: #8d6e63;
+  --md-brown-500: #795548;
+  --md-brown-600: #6d4c41;
+  --md-brown-700: #5d4037;
+  --md-brown-800: #4e342e;
+  --md-brown-900: #3e2723;
+  --md-grey-50: #fafafa;
+  --md-grey-100: #f5f5f5;
+  --md-grey-200: #eee;
+  --md-grey-300: #e0e0e0;
+  --md-grey-400: #bdbdbd;
+  --md-grey-500: #9e9e9e;
+  --md-grey-600: #757575;
+  --md-grey-700: #616161;
+  --md-grey-800: #424242;
+  --md-grey-900: #212121;
+  --md-blue-grey-50: #eceff1;
+  --md-blue-grey-100: #cfd8dc;
+  --md-blue-grey-200: #b0bec5;
+  --md-blue-grey-300: #90a4ae;
+  --md-blue-grey-400: #78909c;
+  --md-blue-grey-500: #607d8b;
+  --md-blue-grey-600: #546e7a;
+  --md-blue-grey-700: #455a64;
+  --md-blue-grey-800: #37474f;
+  --md-blue-grey-900: #263238;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| RenderedText
+|----------------------------------------------------------------------------*/
+
+:root {
+  /* This is the padding value to fill the gaps between lines containing spans with background color. */
+  --jp-private-code-span-padding: calc(
+    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
+  );
+}
+
+.jp-RenderedText {
+  text-align: left;
+  padding-left: var(--jp-code-padding);
+  line-height: var(--jp-code-line-height);
+  font-family: var(--jp-code-font-family);
+}
+
+.jp-RenderedText pre,
+.jp-RenderedJavaScript pre,
+.jp-RenderedHTMLCommon pre {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-code-font-size);
+  border: none;
+  margin: 0;
+  padding: 0;
+}
+
+.jp-RenderedText pre a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedText pre a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedText pre a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* console foregrounds and backgrounds */
+.jp-RenderedText pre .ansi-black-fg {
+  color: #3e424d;
+}
+
+.jp-RenderedText pre .ansi-red-fg {
+  color: #e75c58;
+}
+
+.jp-RenderedText pre .ansi-green-fg {
+  color: #00a250;
+}
+
+.jp-RenderedText pre .ansi-yellow-fg {
+  color: #ddb62b;
+}
+
+.jp-RenderedText pre .ansi-blue-fg {
+  color: #208ffb;
+}
+
+.jp-RenderedText pre .ansi-magenta-fg {
+  color: #d160c4;
+}
+
+.jp-RenderedText pre .ansi-cyan-fg {
+  color: #60c6c8;
+}
+
+.jp-RenderedText pre .ansi-white-fg {
+  color: #c5c1b4;
+}
+
+.jp-RenderedText pre .ansi-black-bg {
+  background-color: #3e424d;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-red-bg {
+  background-color: #e75c58;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-green-bg {
+  background-color: #00a250;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-yellow-bg {
+  background-color: #ddb62b;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-blue-bg {
+  background-color: #208ffb;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-magenta-bg {
+  background-color: #d160c4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-cyan-bg {
+  background-color: #60c6c8;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-white-bg {
+  background-color: #c5c1b4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-black-intense-fg {
+  color: #282c36;
+}
+
+.jp-RenderedText pre .ansi-red-intense-fg {
+  color: #b22b31;
+}
+
+.jp-RenderedText pre .ansi-green-intense-fg {
+  color: #007427;
+}
+
+.jp-RenderedText pre .ansi-yellow-intense-fg {
+  color: #b27d12;
+}
+
+.jp-RenderedText pre .ansi-blue-intense-fg {
+  color: #0065ca;
+}
+
+.jp-RenderedText pre .ansi-magenta-intense-fg {
+  color: #a03196;
+}
+
+.jp-RenderedText pre .ansi-cyan-intense-fg {
+  color: #258f8f;
+}
+
+.jp-RenderedText pre .ansi-white-intense-fg {
+  color: #a1a6b2;
+}
+
+.jp-RenderedText pre .ansi-black-intense-bg {
+  background-color: #282c36;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-red-intense-bg {
+  background-color: #b22b31;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-green-intense-bg {
+  background-color: #007427;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-yellow-intense-bg {
+  background-color: #b27d12;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-blue-intense-bg {
+  background-color: #0065ca;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-magenta-intense-bg {
+  background-color: #a03196;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-cyan-intense-bg {
+  background-color: #258f8f;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-white-intense-bg {
+  background-color: #a1a6b2;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-default-inverse-fg {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.jp-RenderedText pre .ansi-default-inverse-bg {
+  background-color: var(--jp-inverse-layout-color0);
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-bold {
+  font-weight: bold;
+}
+
+.jp-RenderedText pre .ansi-underline {
+  text-decoration: underline;
+}
+
+.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
+  background: var(--jp-rendermime-error-background);
+  padding-top: var(--jp-code-padding);
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedLatex
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedLatex {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+}
+
+/* Left-justify outputs.*/
+.jp-OutputArea-output.jp-RenderedLatex {
+  padding: var(--jp-code-padding);
+  text-align: left;
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedHTML
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedHTMLCommon {
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-content-font-family);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+
+  /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
+  padding-right: 20px;
+}
+
+.jp-RenderedHTMLCommon em {
+  font-style: italic;
+}
+
+.jp-RenderedHTMLCommon strong {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon u {
+  text-decoration: underline;
+}
+
+.jp-RenderedHTMLCommon a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* Headings */
+
+.jp-RenderedHTMLCommon h1,
+.jp-RenderedHTMLCommon h2,
+.jp-RenderedHTMLCommon h3,
+.jp-RenderedHTMLCommon h4,
+.jp-RenderedHTMLCommon h5,
+.jp-RenderedHTMLCommon h6 {
+  line-height: var(--jp-content-heading-line-height);
+  font-weight: var(--jp-content-heading-font-weight);
+  font-style: normal;
+  margin: var(--jp-content-heading-margin-top) 0
+    var(--jp-content-heading-margin-bottom) 0;
+}
+
+.jp-RenderedHTMLCommon h1:first-child,
+.jp-RenderedHTMLCommon h2:first-child,
+.jp-RenderedHTMLCommon h3:first-child,
+.jp-RenderedHTMLCommon h4:first-child,
+.jp-RenderedHTMLCommon h5:first-child,
+.jp-RenderedHTMLCommon h6:first-child {
+  margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
+}
+
+.jp-RenderedHTMLCommon h1:last-child,
+.jp-RenderedHTMLCommon h2:last-child,
+.jp-RenderedHTMLCommon h3:last-child,
+.jp-RenderedHTMLCommon h4:last-child,
+.jp-RenderedHTMLCommon h5:last-child,
+.jp-RenderedHTMLCommon h6:last-child {
+  margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
+}
+
+.jp-RenderedHTMLCommon h1 {
+  font-size: var(--jp-content-font-size5);
+}
+
+.jp-RenderedHTMLCommon h2 {
+  font-size: var(--jp-content-font-size4);
+}
+
+.jp-RenderedHTMLCommon h3 {
+  font-size: var(--jp-content-font-size3);
+}
+
+.jp-RenderedHTMLCommon h4 {
+  font-size: var(--jp-content-font-size2);
+}
+
+.jp-RenderedHTMLCommon h5 {
+  font-size: var(--jp-content-font-size1);
+}
+
+.jp-RenderedHTMLCommon h6 {
+  font-size: var(--jp-content-font-size0);
+}
+
+/* Lists */
+
+/* stylelint-disable selector-max-type, selector-max-compound-selectors */
+
+.jp-RenderedHTMLCommon ul:not(.list-inline),
+.jp-RenderedHTMLCommon ol:not(.list-inline) {
+  padding-left: 2em;
+}
+
+.jp-RenderedHTMLCommon ul {
+  list-style: disc;
+}
+
+.jp-RenderedHTMLCommon ul ul {
+  list-style: square;
+}
+
+.jp-RenderedHTMLCommon ul ul ul {
+  list-style: circle;
+}
+
+.jp-RenderedHTMLCommon ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol ol {
+  list-style: upper-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol {
+  list-style: lower-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol {
+  list-style: lower-roman;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol,
+.jp-RenderedHTMLCommon ul {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon ul ul,
+.jp-RenderedHTMLCommon ul ol,
+.jp-RenderedHTMLCommon ol ul,
+.jp-RenderedHTMLCommon ol ol {
+  margin-bottom: 0;
+}
+
+/* stylelint-enable selector-max-type, selector-max-compound-selectors */
+
+.jp-RenderedHTMLCommon hr {
+  color: var(--jp-border-color2);
+  background-color: var(--jp-border-color1);
+  margin-top: 1em;
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon > pre {
+  margin: 1.5em 2em;
+}
+
+.jp-RenderedHTMLCommon pre,
+.jp-RenderedHTMLCommon code {
+  border: 0;
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  line-height: var(--jp-code-line-height);
+  padding: 0;
+  white-space: pre-wrap;
+}
+
+.jp-RenderedHTMLCommon :not(pre) > code {
+  background-color: var(--jp-layout-color2);
+  padding: 1px 5px;
+}
+
+/* Tables */
+
+.jp-RenderedHTMLCommon table {
+  border-collapse: collapse;
+  border-spacing: 0;
+  border: none;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  table-layout: fixed;
+  margin-left: auto;
+  margin-bottom: 1em;
+  margin-right: auto;
+}
+
+.jp-RenderedHTMLCommon thead {
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  vertical-align: bottom;
+}
+
+.jp-RenderedHTMLCommon td,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon tr {
+  vertical-align: middle;
+  padding: 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
+  max-width: none;
+}
+
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
+  text-align: right;
+}
+
+.jp-RenderedHTMLCommon th {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
+  background: var(--jp-layout-color0);
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
+  background: var(--jp-rendermime-table-row-background);
+}
+
+.jp-RenderedHTMLCommon tbody tr:hover {
+  background: var(--jp-rendermime-table-row-hover-background);
+}
+
+.jp-RenderedHTMLCommon p {
+  text-align: left;
+  margin: 0;
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon img {
+  -moz-force-broken-image-icon: 1;
+}
+
+/* Restrict to direct children as other images could be nested in other content. */
+.jp-RenderedHTMLCommon > img {
+  display: block;
+  margin-left: 0;
+  margin-right: 0;
+  margin-bottom: 1em;
+}
+
+/* Change color behind transparent images if they need it... */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+
+.jp-RenderedHTMLCommon img,
+.jp-RenderedImage img,
+.jp-RenderedHTMLCommon svg,
+.jp-RenderedSVG svg {
+  max-width: 100%;
+  height: auto;
+}
+
+.jp-RenderedHTMLCommon img.jp-mod-unconfined,
+.jp-RenderedImage img.jp-mod-unconfined,
+.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
+.jp-RenderedSVG svg.jp-mod-unconfined {
+  max-width: none;
+}
+
+.jp-RenderedHTMLCommon .alert {
+  padding: var(--jp-notebook-padding);
+  border: var(--jp-border-width) solid transparent;
+  border-radius: var(--jp-border-radius);
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon .alert-info {
+  color: var(--jp-info-color0);
+  background-color: var(--jp-info-color3);
+  border-color: var(--jp-info-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-info hr {
+  border-color: var(--jp-info-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-info > p:last-child,
+.jp-RenderedHTMLCommon .alert-info > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-warning {
+  color: var(--jp-warn-color0);
+  background-color: var(--jp-warn-color3);
+  border-color: var(--jp-warn-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-warning hr {
+  border-color: var(--jp-warn-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-warning > p:last-child,
+.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-success {
+  color: var(--jp-success-color0);
+  background-color: var(--jp-success-color3);
+  border-color: var(--jp-success-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-success hr {
+  border-color: var(--jp-success-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-success > p:last-child,
+.jp-RenderedHTMLCommon .alert-success > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-danger {
+  color: var(--jp-error-color0);
+  background-color: var(--jp-error-color3);
+  border-color: var(--jp-error-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-danger hr {
+  border-color: var(--jp-error-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-danger > p:last-child,
+.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon blockquote {
+  margin: 1em 2em;
+  padding: 0 1em;
+  border-left: 5px solid var(--jp-border-color2);
+}
+
+a.jp-InternalAnchorLink {
+  visibility: hidden;
+  margin-left: 8px;
+  color: var(--md-blue-800);
+}
+
+h1:hover .jp-InternalAnchorLink,
+h2:hover .jp-InternalAnchorLink,
+h3:hover .jp-InternalAnchorLink,
+h4:hover .jp-InternalAnchorLink,
+h5:hover .jp-InternalAnchorLink,
+h6:hover .jp-InternalAnchorLink {
+  visibility: visible;
+}
+
+.jp-RenderedHTMLCommon kbd {
+  background-color: var(--jp-rendermime-table-row-background);
+  border: 1px solid var(--jp-border-color0);
+  border-bottom-color: var(--jp-border-color2);
+  border-radius: 3px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+  display: inline-block;
+  font-size: var(--jp-ui-font-size0);
+  line-height: 1em;
+  padding: 0.2em 0.5em;
+}
+
+/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
+ * At the bottom of cells this is a bit too much as there is also spacing
+ * between cells. Going all the way to 0 gets too tight between markdown and
+ * code cells.
+ */
+.jp-RenderedHTMLCommon > *:last-child {
+  margin-bottom: 0.5em;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-cursor-backdrop {
+  position: fixed;
+  width: 200px;
+  height: 200px;
+  margin-top: -100px;
+  margin-left: -100px;
+  will-change: transform;
+  z-index: 100;
+}
+
+.lm-mod-drag-image {
+  will-change: transform;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-lineFormSearch {
+  padding: 4px 12px;
+  background-color: var(--jp-layout-color2);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
+  font-size: var(--jp-ui-font-size1);
+}
+
+.jp-lineFormCaption {
+  font-size: var(--jp-ui-font-size0);
+  line-height: var(--jp-ui-font-size1);
+  margin-top: 4px;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-baseLineForm {
+  border: none;
+  border-radius: 0;
+  position: absolute;
+  background-size: 16px;
+  background-repeat: no-repeat;
+  background-position: center;
+  outline: none;
+}
+
+.jp-lineFormButtonContainer {
+  top: 4px;
+  right: 8px;
+  height: 24px;
+  padding: 0 12px;
+  width: 12px;
+}
+
+.jp-lineFormButtonIcon {
+  top: 0;
+  right: 0;
+  background-color: var(--jp-brand-color1);
+  height: 100%;
+  width: 100%;
+  box-sizing: border-box;
+  padding: 4px 6px;
+}
+
+.jp-lineFormButton {
+  top: 0;
+  right: 0;
+  background-color: transparent;
+  height: 100%;
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.jp-lineFormWrapper {
+  overflow: hidden;
+  padding: 0 8px;
+  border: 1px solid var(--jp-border-color0);
+  background-color: var(--jp-input-active-background);
+  height: 22px;
+}
+
+.jp-lineFormWrapperFocusWithin {
+  border: var(--jp-border-width) solid var(--md-blue-500);
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-lineFormInput {
+  background: transparent;
+  width: 200px;
+  height: 100%;
+  border: none;
+  outline: none;
+  color: var(--jp-ui-font-color0);
+  line-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-JSONEditor {
+  display: flex;
+  flex-direction: column;
+  width: 100%;
+}
+
+.jp-JSONEditor-host {
+  flex: 1 1 auto;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0;
+  background: var(--jp-layout-color0);
+  min-height: 50px;
+  padding: 1px;
+}
+
+.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
+  border-color: red;
+  outline-color: red;
+}
+
+.jp-JSONEditor-header {
+  display: flex;
+  flex: 1 0 auto;
+  padding: 0 0 0 12px;
+}
+
+.jp-JSONEditor-header label {
+  flex: 0 0 auto;
+}
+
+.jp-JSONEditor-commitButton {
+  height: 16px;
+  width: 16px;
+  background-size: 18px;
+  background-repeat: no-repeat;
+  background-position: center;
+}
+
+.jp-JSONEditor-host.jp-mod-focused {
+  background-color: var(--jp-input-active-background);
+  border: 1px solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+.jp-Editor.jp-mod-dropTarget {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+.jp-DocumentSearch-input {
+  border: none;
+  outline: none;
+  color: var(--jp-ui-font-color0);
+  font-size: var(--jp-ui-font-size1);
+  background-color: var(--jp-layout-color0);
+  font-family: var(--jp-ui-font-family);
+  padding: 2px 1px;
+  resize: none;
+}
+
+.jp-DocumentSearch-overlay {
+  position: absolute;
+  background-color: var(--jp-toolbar-background);
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  top: 0;
+  right: 0;
+  z-index: 7;
+  min-width: 405px;
+  padding: 2px;
+  font-size: var(--jp-ui-font-size1);
+
+  --jp-private-document-search-button-height: 20px;
+}
+
+.jp-DocumentSearch-overlay button {
+  background-color: var(--jp-toolbar-background);
+  outline: 0;
+}
+
+.jp-DocumentSearch-overlay button:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-overlay button:active {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-overlay-row {
+  display: flex;
+  align-items: center;
+  margin-bottom: 2px;
+}
+
+.jp-DocumentSearch-button-content {
+  display: inline-block;
+  cursor: pointer;
+  box-sizing: border-box;
+  width: 100%;
+  height: 100%;
+}
+
+.jp-DocumentSearch-button-content svg {
+  width: 100%;
+  height: 100%;
+}
+
+.jp-DocumentSearch-input-wrapper {
+  border: var(--jp-border-width) solid var(--jp-border-color0);
+  display: flex;
+  background-color: var(--jp-layout-color0);
+  margin: 2px;
+}
+
+.jp-DocumentSearch-input-wrapper:focus-within {
+  border-color: var(--jp-cell-editor-active-border-color);
+}
+
+.jp-DocumentSearch-toggle-wrapper,
+.jp-DocumentSearch-button-wrapper {
+  all: initial;
+  overflow: hidden;
+  display: inline-block;
+  border: none;
+  box-sizing: border-box;
+}
+
+.jp-DocumentSearch-toggle-wrapper {
+  width: 14px;
+  height: 14px;
+}
+
+.jp-DocumentSearch-button-wrapper {
+  width: var(--jp-private-document-search-button-height);
+  height: var(--jp-private-document-search-button-height);
+}
+
+.jp-DocumentSearch-toggle-wrapper:focus,
+.jp-DocumentSearch-button-wrapper:focus {
+  outline: var(--jp-border-width) solid
+    var(--jp-cell-editor-active-border-color);
+  outline-offset: -1px;
+}
+
+.jp-DocumentSearch-toggle-wrapper,
+.jp-DocumentSearch-button-wrapper,
+.jp-DocumentSearch-button-content:focus {
+  outline: none;
+}
+
+.jp-DocumentSearch-toggle-placeholder {
+  width: 5px;
+}
+
+.jp-DocumentSearch-input-button::before {
+  display: block;
+  padding-top: 100%;
+}
+
+.jp-DocumentSearch-input-button-off {
+  opacity: var(--jp-search-toggle-off-opacity);
+}
+
+.jp-DocumentSearch-input-button-off:hover {
+  opacity: var(--jp-search-toggle-hover-opacity);
+}
+
+.jp-DocumentSearch-input-button-on {
+  opacity: var(--jp-search-toggle-on-opacity);
+}
+
+.jp-DocumentSearch-index-counter {
+  padding-left: 10px;
+  padding-right: 10px;
+  user-select: none;
+  min-width: 35px;
+  display: inline-block;
+}
+
+.jp-DocumentSearch-up-down-wrapper {
+  display: inline-block;
+  padding-right: 2px;
+  margin-left: auto;
+  white-space: nowrap;
+}
+
+.jp-DocumentSearch-spacer {
+  margin-left: auto;
+}
+
+.jp-DocumentSearch-up-down-wrapper button {
+  outline: 0;
+  border: none;
+  width: var(--jp-private-document-search-button-height);
+  height: var(--jp-private-document-search-button-height);
+  vertical-align: middle;
+  margin: 1px 5px 2px;
+}
+
+.jp-DocumentSearch-up-down-button:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-up-down-button:active {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-filter-button {
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-DocumentSearch-filter-button:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-filter-button-enabled {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-filter-button-enabled:hover {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-search-options {
+  padding: 0 8px;
+  margin-left: 3px;
+  width: 100%;
+  display: grid;
+  justify-content: start;
+  grid-template-columns: 1fr 1fr;
+  align-items: center;
+  justify-items: stretch;
+}
+
+.jp-DocumentSearch-search-filter-disabled {
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-DocumentSearch-search-filter {
+  display: flex;
+  align-items: center;
+  user-select: none;
+}
+
+.jp-DocumentSearch-regex-error {
+  color: var(--jp-error-color0);
+}
+
+.jp-DocumentSearch-replace-button-wrapper {
+  overflow: hidden;
+  display: inline-block;
+  box-sizing: border-box;
+  border: var(--jp-border-width) solid var(--jp-border-color0);
+  margin: auto 2px;
+  padding: 1px 4px;
+  height: calc(var(--jp-private-document-search-button-height) + 2px);
+}
+
+.jp-DocumentSearch-replace-button-wrapper:focus {
+  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+}
+
+.jp-DocumentSearch-replace-button {
+  display: inline-block;
+  text-align: center;
+  cursor: pointer;
+  box-sizing: border-box;
+  color: var(--jp-ui-font-color1);
+
+  /* height - 2 * (padding of wrapper) */
+  line-height: calc(var(--jp-private-document-search-button-height) - 2px);
+  width: 100%;
+  height: 100%;
+}
+
+.jp-DocumentSearch-replace-button:focus {
+  outline: none;
+}
+
+.jp-DocumentSearch-replace-wrapper-class {
+  margin-left: 14px;
+  display: flex;
+}
+
+.jp-DocumentSearch-replace-toggle {
+  border: none;
+  background-color: var(--jp-toolbar-background);
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-DocumentSearch-replace-toggle:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.cm-editor {
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  border: 0;
+  border-radius: 0;
+  height: auto;
+
+  /* Changed to auto to autogrow */
+}
+
+.cm-editor pre {
+  padding: 0 var(--jp-code-padding);
+}
+
+.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+}
+
+.jp-CodeMirrorEditor {
+  cursor: text;
+}
+
+/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
+@media screen and (min-width: 2138px) and (max-width: 4319px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
+    border-left: var(--jp-code-cursor-width1) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+/* When zoomed out less than 33% */
+@media screen and (min-width: 4320px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
+    border-left: var(--jp-code-cursor-width2) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+.cm-editor.jp-mod-readOnly .cm-cursor {
+  display: none;
+}
+
+.jp-CollaboratorCursor {
+  border-left: 5px solid transparent;
+  border-right: 5px solid transparent;
+  border-top: none;
+  border-bottom: 3px solid;
+  background-clip: content-box;
+  margin-left: -5px;
+  margin-right: -5px;
+}
+
+.cm-searching,
+.cm-searching span {
+  /* `.cm-searching span`: we need to override syntax highlighting */
+  background-color: var(--jp-search-unselected-match-background-color);
+  color: var(--jp-search-unselected-match-color);
+}
+
+.cm-searching::selection,
+.cm-searching span::selection {
+  background-color: var(--jp-search-unselected-match-background-color);
+  color: var(--jp-search-unselected-match-color);
+}
+
+.jp-current-match > .cm-searching,
+.jp-current-match > .cm-searching span,
+.cm-searching > .jp-current-match,
+.cm-searching > .jp-current-match span {
+  background-color: var(--jp-search-selected-match-background-color);
+  color: var(--jp-search-selected-match-color);
+}
+
+.jp-current-match > .cm-searching::selection,
+.cm-searching > .jp-current-match::selection,
+.jp-current-match > .cm-searching span::selection {
+  background-color: var(--jp-search-selected-match-background-color);
+  color: var(--jp-search-selected-match-color);
+}
+
+.cm-trailingspace {
+  background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
+  background-position: center left;
+  background-repeat: repeat-x;
+}
+
+.jp-CollaboratorCursor-hover {
+  position: absolute;
+  z-index: 1;
+  transform: translateX(-50%);
+  color: white;
+  border-radius: 3px;
+  padding-left: 4px;
+  padding-right: 4px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+  text-align: center;
+  font-size: var(--jp-ui-font-size1);
+  white-space: nowrap;
+}
+
+.jp-CodeMirror-ruler {
+  border-left: 1px dashed var(--jp-border-color2);
+}
+
+/* Styles for shared cursors (remote cursor locations and selected ranges) */
+.jp-CodeMirrorEditor .cm-ySelectionCaret {
+  position: relative;
+  border-left: 1px solid black;
+  margin-left: -1px;
+  margin-right: -1px;
+  box-sizing: border-box;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
+  white-space: nowrap;
+  position: absolute;
+  top: -1.15em;
+  padding-bottom: 0.05em;
+  left: -1px;
+  font-size: 0.95em;
+  font-family: var(--jp-ui-font-family);
+  font-weight: bold;
+  line-height: normal;
+  user-select: none;
+  color: white;
+  padding-left: 2px;
+  padding-right: 2px;
+  z-index: 101;
+  transition: opacity 0.3s ease-in-out;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionInfo {
+  transition-delay: 0.7s;
+  opacity: 0;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
+  opacity: 1;
+  transition-delay: 0s;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MimeDocument {
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-filebrowser-button-height: 28px;
+  --jp-private-filebrowser-button-width: 48px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser .jp-SidePanel-content {
+  display: flex;
+  flex-direction: column;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  flex-wrap: wrap;
+  row-gap: 12px;
+  border-bottom: none;
+  height: auto;
+  margin: 8px 12px 0;
+  box-shadow: none;
+  padding: 0;
+  justify-content: flex-start;
+}
+
+.jp-FileBrowser-Panel {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
+}
+
+.jp-BreadCrumbs {
+  flex: 0 0 auto;
+  margin: 8px 12px;
+}
+
+.jp-BreadCrumbs-item {
+  margin: 0 2px;
+  padding: 0 2px;
+  border-radius: var(--jp-border-radius);
+  cursor: pointer;
+}
+
+.jp-BreadCrumbs-item:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-BreadCrumbs-item:first-child {
+  margin-left: 0;
+}
+
+.jp-BreadCrumbs-item.jp-mod-dropTarget {
+  background-color: var(--jp-brand-color2);
+  opacity: 0.7;
+}
+
+/*-----------------------------------------------------------------------------
+| Buttons
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser-toolbar > .jp-Toolbar-item {
+  flex: 0 0 auto;
+  padding-left: 0;
+  padding-right: 2px;
+  align-items: center;
+  height: unset;
+}
+
+.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
+  width: 40px;
+}
+
+/*-----------------------------------------------------------------------------
+| Other styles
+|----------------------------------------------------------------------------*/
+
+.jp-FileDialog.jp-mod-conflict input {
+  color: var(--jp-error-color1);
+}
+
+.jp-FileDialog .jp-new-name-title {
+  margin-top: 12px;
+}
+
+.jp-LastModified-hidden {
+  display: none;
+}
+
+.jp-FileSize-hidden {
+  display: none;
+}
+
+.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| DirListing
+|----------------------------------------------------------------------------*/
+
+.jp-DirListing {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
+  outline: 0;
+}
+
+.jp-DirListing-header {
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  overflow: hidden;
+  border-top: var(--jp-border-width) solid var(--jp-border-color2);
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
+}
+
+.jp-DirListing-headerItem {
+  padding: 4px 12px 2px;
+  font-weight: 500;
+}
+
+.jp-DirListing-headerItem:hover {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-headerItem.jp-id-name {
+  flex: 1 0 84px;
+}
+
+.jp-DirListing-headerItem.jp-id-modified {
+  flex: 0 0 112px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+}
+
+.jp-DirListing-headerItem.jp-id-filesize {
+  flex: 0 0 75px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+}
+
+.jp-id-narrow {
+  display: none;
+  flex: 0 0 5px;
+  padding: 4px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+  color: var(--jp-border-color2);
+}
+
+.jp-DirListing-narrow .jp-id-narrow {
+  display: block;
+}
+
+.jp-DirListing-narrow .jp-id-modified,
+.jp-DirListing-narrow .jp-DirListing-itemModified {
+  display: none;
+}
+
+.jp-DirListing-headerItem.jp-mod-selected {
+  font-weight: 600;
+}
+
+/* increase specificity to override bundled default */
+.jp-DirListing-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-content mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+/* Style the directory listing content when a user drops a file to upload */
+.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
+  outline: 5px dashed rgba(128, 128, 128, 0.5);
+  outline-offset: -10px;
+  cursor: copy;
+}
+
+.jp-DirListing-item {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  padding: 4px 12px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-DirListing-checkboxWrapper {
+  /* Increases hit area of checkbox. */
+  padding: 4px;
+}
+
+.jp-DirListing-header
+  .jp-DirListing-checkboxWrapper
+  + .jp-DirListing-headerItem {
+  padding-left: 4px;
+}
+
+.jp-DirListing-content .jp-DirListing-checkboxWrapper {
+  position: relative;
+  left: -4px;
+  margin: -4px 0 -4px -8px;
+}
+
+.jp-DirListing-checkboxWrapper.jp-mod-visible {
+  visibility: visible;
+}
+
+/* For devices that support hovering, hide checkboxes until hovered, selected...
+*/
+@media (hover: hover) {
+  .jp-DirListing-checkboxWrapper {
+    visibility: hidden;
+  }
+
+  .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
+  .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
+    visibility: visible;
+  }
+}
+
+.jp-DirListing-item[data-is-dot] {
+  opacity: 75%;
+}
+
+.jp-DirListing-item.jp-mod-selected {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.jp-DirListing-item.jp-mod-dropTarget {
+  background: var(--jp-brand-color3);
+}
+
+.jp-DirListing-item:hover:not(.jp-mod-selected) {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-itemIcon {
+  flex: 0 0 20px;
+  margin-right: 4px;
+}
+
+.jp-DirListing-itemText {
+  flex: 1 0 64px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  user-select: none;
+}
+
+.jp-DirListing-itemText:focus {
+  outline-width: 2px;
+  outline-color: var(--jp-inverse-layout-color1);
+  outline-style: solid;
+  outline-offset: 1px;
+}
+
+.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
+  outline-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-itemModified {
+  flex: 0 0 125px;
+  text-align: right;
+}
+
+.jp-DirListing-itemFileSize {
+  flex: 0 0 90px;
+  text-align: right;
+}
+
+.jp-DirListing-editor {
+  flex: 1 0 64px;
+  outline: none;
+  border: none;
+  color: var(--jp-ui-font-color1);
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
+  color: var(--jp-success-color1);
+  content: '\25CF';
+  font-size: 8px;
+  position: absolute;
+  left: -8px;
+}
+
+.jp-DirListing-item.jp-mod-running.jp-mod-selected
+  .jp-DirListing-itemIcon::before {
+  color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-DirListing-item.lm-mod-drag-image,
+.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
+  font-size: var(--jp-ui-font-size1);
+  padding-left: 4px;
+  margin-left: 4px;
+  width: 160px;
+  background-color: var(--jp-ui-inverse-font-color2);
+  box-shadow: var(--jp-elevation-z2);
+  border-radius: 0;
+  color: var(--jp-ui-font-color1);
+  transform: translateX(-40%) translateY(-58%);
+}
+
+.jp-Document {
+  min-width: 120px;
+  min-height: 120px;
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Main OutputArea
+| OutputArea has a list of Outputs
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea {
+  overflow-y: auto;
+}
+
+.jp-OutputArea-child {
+  display: table;
+  table-layout: fixed;
+  width: 100%;
+  overflow: hidden;
+}
+
+.jp-OutputPrompt {
+  width: var(--jp-cell-prompt-width);
+  color: var(--jp-cell-outprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+  opacity: var(--jp-cell-prompt-opacity);
+
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-OutputArea-prompt {
+  display: table-cell;
+  vertical-align: top;
+}
+
+.jp-OutputArea-output {
+  display: table-cell;
+  width: 100%;
+  height: auto;
+  overflow: auto;
+  user-select: text;
+  -moz-user-select: text;
+  -webkit-user-select: text;
+  -ms-user-select: text;
+}
+
+.jp-OutputArea .jp-RenderedText {
+  padding-left: 1ch;
+}
+
+/**
+ * Prompt overlay.
+ */
+
+.jp-OutputArea-promptOverlay {
+  position: absolute;
+  top: 0;
+  width: var(--jp-cell-prompt-width);
+  height: 100%;
+  opacity: 0.5;
+}
+
+.jp-OutputArea-promptOverlay:hover {
+  background: var(--jp-layout-color2);
+  box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
+  cursor: zoom-out;
+}
+
+.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
+  cursor: zoom-in;
+}
+
+/**
+ * Isolated output.
+ */
+.jp-OutputArea-output.jp-mod-isolated {
+  width: 100%;
+  display: block;
+}
+
+/*
+When drag events occur, `lm-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+/* pre */
+
+.jp-OutputArea-output pre {
+  border: none;
+  margin: 0;
+  padding: 0;
+  overflow-x: auto;
+  overflow-y: auto;
+  word-break: break-all;
+  word-wrap: break-word;
+  white-space: pre-wrap;
+}
+
+/* tables */
+
+.jp-OutputArea-output.jp-RenderedHTMLCommon table {
+  margin-left: 0;
+  margin-right: 0;
+}
+
+/* description lists */
+
+.jp-OutputArea-output dl,
+.jp-OutputArea-output dt,
+.jp-OutputArea-output dd {
+  display: block;
+}
+
+.jp-OutputArea-output dl {
+  width: 100%;
+  overflow: hidden;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dt {
+  font-weight: bold;
+  float: left;
+  width: 20%;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dd {
+  float: left;
+  width: 80%;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-TrimmedOutputs pre {
+  background: var(--jp-layout-color3);
+  font-size: calc(var(--jp-code-font-size) * 1.4);
+  text-align: center;
+  text-transform: uppercase;
+}
+
+/* Hide the gutter in case of
+ *  - nested output areas (e.g. in the case of output widgets)
+ *  - mirrored output areas
+ */
+.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
+  display: none;
+}
+
+/* Hide empty lines in the output area, for instance due to cleared widgets */
+.jp-OutputArea-prompt:empty {
+  padding: 0;
+  border: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| executeResult is added to any Output-result for the display of the object
+| returned by a cell
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea-output.jp-OutputArea-executeResult {
+  margin-left: 0;
+  width: 100%;
+}
+
+/* Text output with the Out[] prompt needs a top padding to match the
+ * alignment of the Out[] prompt itself.
+ */
+.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
+  padding-top: var(--jp-code-padding);
+  border-top: var(--jp-border-width) solid transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| The Stdin output
+|----------------------------------------------------------------------------*/
+
+.jp-Stdin-prompt {
+  color: var(--jp-content-font-color0);
+  padding-right: var(--jp-code-padding);
+  vertical-align: baseline;
+  flex: 0 0 auto;
+}
+
+.jp-Stdin-input {
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  color: inherit;
+  background-color: inherit;
+  width: 42%;
+  min-width: 200px;
+
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0 0.25em;
+  margin: 0 0.25em;
+  flex: 0 0 70%;
+}
+
+.jp-Stdin-input::placeholder {
+  opacity: 0;
+}
+
+.jp-Stdin-input:focus {
+  box-shadow: none;
+}
+
+.jp-Stdin-input:focus::placeholder {
+  opacity: 1;
+}
+
+/*-----------------------------------------------------------------------------
+| Output Area View
+|----------------------------------------------------------------------------*/
+
+.jp-LinkedOutputView .jp-OutputArea {
+  height: 100%;
+  display: block;
+}
+
+.jp-LinkedOutputView .jp-OutputArea-output:only-child {
+  height: 100%;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+@media print {
+  .jp-OutputArea-child {
+    break-inside: avoid-page;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+  .jp-OutputPrompt {
+    display: table-row;
+    text-align: left;
+  }
+
+  .jp-OutputArea-child .jp-OutputArea-output {
+    display: table-row;
+    margin-left: var(--jp-notebook-padding);
+  }
+}
+
+/* Trimmed outputs warning */
+.jp-TrimmedOutputs > a {
+  margin: 10px;
+  text-decoration: none;
+  cursor: pointer;
+}
+
+.jp-TrimmedOutputs > a:hover {
+  text-decoration: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Table of Contents
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toc-active-width: 4px;
+}
+
+.jp-TableOfContents {
+  display: flex;
+  flex-direction: column;
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  height: 100%;
+}
+
+.jp-TableOfContents-placeholder {
+  text-align: center;
+}
+
+.jp-TableOfContents-placeholderContent {
+  color: var(--jp-content-font-color2);
+  padding: 8px;
+}
+
+.jp-TableOfContents-placeholderContent > h3 {
+  margin-bottom: var(--jp-content-heading-margin-bottom);
+}
+
+.jp-TableOfContents .jp-SidePanel-content {
+  overflow-y: auto;
+}
+
+.jp-TableOfContents-tree {
+  margin: 4px;
+}
+
+.jp-TableOfContents ol {
+  list-style-type: none;
+}
+
+/* stylelint-disable-next-line selector-max-type */
+.jp-TableOfContents li > ol {
+  /* Align left border with triangle icon center */
+  padding-left: 11px;
+}
+
+.jp-TableOfContents-content {
+  /* left margin for the active heading indicator */
+  margin: 0 0 0 var(--jp-private-toc-active-width);
+  padding: 0;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-tocItem {
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-tocItem-heading {
+  display: flex;
+  cursor: pointer;
+}
+
+.jp-tocItem-heading:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-tocItem-content {
+  display: block;
+  padding: 4px 0;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+  overflow-x: hidden;
+}
+
+.jp-tocItem-collapser {
+  height: 20px;
+  margin: 2px 2px 0;
+  padding: 0;
+  background: none;
+  border: none;
+  cursor: pointer;
+}
+
+.jp-tocItem-collapser:hover {
+  background-color: var(--jp-layout-color3);
+}
+
+/* Active heading indicator */
+
+.jp-tocItem-heading::before {
+  content: ' ';
+  background: transparent;
+  width: var(--jp-private-toc-active-width);
+  height: 24px;
+  position: absolute;
+  left: 0;
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-tocItem-heading.jp-tocItem-active::before {
+  background-color: var(--jp-brand-color1);
+}
+
+.jp-tocItem-heading:hover.jp-tocItem-active::before {
+  background: var(--jp-brand-color0);
+  opacity: 1;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapser {
+  flex: 0 0 var(--jp-cell-collapser-width);
+  padding: 0;
+  margin: 0;
+  border: none;
+  outline: none;
+  background: transparent;
+  border-radius: var(--jp-border-radius);
+  opacity: 1;
+}
+
+.jp-Collapser-child {
+  display: block;
+  width: 100%;
+  box-sizing: border-box;
+
+  /* height: 100% doesn't work because the height of its parent is computed from content */
+  position: absolute;
+  top: 0;
+  bottom: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Hiding collapsers in print mode.
+
+Note: input and output wrappers have "display: block" propery in print mode.
+*/
+
+@media print {
+  .jp-Collapser {
+    display: none;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Header/Footer
+|----------------------------------------------------------------------------*/
+
+/* Hidden by zero height by default */
+.jp-CellHeader,
+.jp-CellFooter {
+  height: 0;
+  width: 100%;
+  padding: 0;
+  margin: 0;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Input
+|----------------------------------------------------------------------------*/
+
+/* All input areas */
+.jp-InputArea {
+  display: table;
+  table-layout: fixed;
+  width: 100%;
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  display: table-cell;
+  overflow: hidden;
+  vertical-align: top;
+
+  /* This is the non-active, default styling */
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  border-radius: 0;
+  background: var(--jp-cell-editor-background);
+}
+
+.jp-InputPrompt {
+  display: table-cell;
+  vertical-align: top;
+  width: var(--jp-cell-prompt-width);
+  color: var(--jp-cell-inprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  opacity: var(--jp-cell-prompt-opacity);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+  .jp-InputArea-editor {
+    display: table-row;
+    margin-left: var(--jp-notebook-padding);
+  }
+
+  .jp-InputPrompt {
+    display: table-row;
+    text-align: left;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Placeholder {
+  display: table;
+  table-layout: fixed;
+  width: 100%;
+}
+
+.jp-Placeholder-prompt {
+  display: table-cell;
+  box-sizing: border-box;
+}
+
+.jp-Placeholder-content {
+  display: table-cell;
+  padding: 4px 6px;
+  border: 1px solid transparent;
+  border-radius: 0;
+  background: none;
+  box-sizing: border-box;
+  cursor: pointer;
+}
+
+.jp-Placeholder-contentContainer {
+  display: flex;
+}
+
+.jp-Placeholder-content:hover,
+.jp-InputPlaceholder > .jp-Placeholder-content:hover {
+  border-color: var(--jp-layout-color3);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon {
+  width: 32px;
+  height: 16px;
+  border: 1px solid transparent;
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon:hover {
+  border: 1px solid var(--jp-border-color1);
+  box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
+  background-color: var(--jp-layout-color0);
+}
+
+.jp-PlaceholderText {
+  white-space: nowrap;
+  overflow-x: hidden;
+  color: var(--jp-inverse-layout-color3);
+  font-family: var(--jp-code-font-family);
+}
+
+.jp-InputPlaceholder > .jp-Placeholder-content {
+  border-color: var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-cell-scrolling-output-offset: 5px;
+}
+
+/*-----------------------------------------------------------------------------
+| Cell
+|----------------------------------------------------------------------------*/
+
+.jp-Cell {
+  padding: var(--jp-cell-padding);
+  margin: 0;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Common input/output
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-inputWrapper,
+.jp-Cell-outputWrapper {
+  display: flex;
+  flex-direction: row;
+  padding: 0;
+  margin: 0;
+
+  /* Added to reveal the box-shadow on the input and output collapsers. */
+  overflow: visible;
+}
+
+/* Only input/output areas inside cells */
+.jp-Cell-inputArea,
+.jp-Cell-outputArea {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Collapser
+|----------------------------------------------------------------------------*/
+
+/* Make the output collapser disappear when there is not output, but do so
+ * in a manner that leaves it in the layout and preserves its width.
+ */
+.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
+  border: none !important;
+  background: transparent !important;
+}
+
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
+  min-height: var(--jp-cell-collapser-min-height);
+}
+
+/*-----------------------------------------------------------------------------
+| Output
+|----------------------------------------------------------------------------*/
+
+/* Put a space between input and output when there IS output */
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
+  margin-top: 5px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
+  overflow-y: auto;
+  max-height: 24em;
+  margin-left: var(--jp-private-cell-scrolling-output-offset);
+  resize: vertical;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
+  max-height: unset;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
+  content: ' ';
+  box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
+  width: 100%;
+  height: 100%;
+  position: sticky;
+  bottom: 0;
+  top: 0;
+  margin-top: -50%;
+  float: left;
+  display: block;
+  pointer-events: none;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
+  padding-top: 6px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+  width: calc(
+    var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
+  );
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
+  left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
+}
+
+/*-----------------------------------------------------------------------------
+| CodeCell
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| MarkdownCell
+|----------------------------------------------------------------------------*/
+
+.jp-MarkdownOutput {
+  display: table-cell;
+  width: 100%;
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-left: var(--jp-code-padding);
+}
+
+.jp-MarkdownOutput.jp-RenderedHTMLCommon {
+  overflow: auto;
+}
+
+/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
+.jp-collapseHeadingButton {
+  display: flex;
+  min-height: var(--jp-cell-collapser-min-height);
+  font-size: var(--jp-code-font-size);
+  position: absolute;
+  background-color: transparent;
+  background-size: 25px;
+  background-repeat: no-repeat;
+  background-position-x: center;
+  background-position-y: top;
+  background-image: var(--jp-icon-caret-down);
+  right: 0;
+  top: 0;
+  bottom: 0;
+}
+
+.jp-collapseHeadingButton.jp-mod-collapsed {
+  background-image: var(--jp-icon-caret-right);
+}
+
+/*
+ set the container font size to match that of content
+ so that the nested collapse buttons have the right size
+*/
+.jp-MarkdownCell .jp-InputPrompt {
+  font-size: var(--jp-content-font-size1);
+}
+
+/*
+  Align collapseHeadingButton with cell top header
+  The font sizes are identical to the ones in packages/rendermime/style/base.css
+*/
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
+  font-size: var(--jp-content-font-size5);
+  background-position-y: calc(0.3 * var(--jp-content-font-size5));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
+  font-size: var(--jp-content-font-size4);
+  background-position-y: calc(0.3 * var(--jp-content-font-size4));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
+  font-size: var(--jp-content-font-size3);
+  background-position-y: calc(0.3 * var(--jp-content-font-size3));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
+  font-size: var(--jp-content-font-size2);
+  background-position-y: calc(0.3 * var(--jp-content-font-size2));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
+  font-size: var(--jp-content-font-size1);
+  background-position-y: top;
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
+  font-size: var(--jp-content-font-size0);
+  background-position-y: top;
+}
+
+/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
+  display: none;
+}
+
+.jp-Notebook.jp-mod-showHiddenCellsButton
+  :is(.jp-MarkdownCell:hover, .jp-mod-active)
+  .jp-collapseHeadingButton {
+  display: flex;
+}
+
+/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
+is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
+  margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
+  margin-top: var(--jp-code-padding);
+  border: 1px solid var(--jp-border-color2);
+  background-color: var(--jp-border-color3) !important;
+  color: var(--jp-content-font-color0) !important;
+  display: flex;
+}
+
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
+  background-color: var(--jp-border-color2) !important;
+}
+
+.jp-showHiddenCellsButton {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Using block instead of flex to allow the use of the break-inside CSS property for
+cell outputs.
+*/
+
+@media print {
+  .jp-Cell-inputWrapper,
+  .jp-Cell-outputWrapper {
+    display: block;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-notebook-toolbar-padding: 2px 5px 2px 2px;
+}
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel-toolbar {
+  padding: var(--jp-notebook-toolbar-padding);
+
+  /* disable paint containment from lumino 2.0 default strict CSS containment */
+  contain: style size !important;
+}
+
+.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
+  border: none;
+  box-shadow: none;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown select {
+  height: 24px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: 14px;
+  border-radius: 0;
+  display: block;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown span {
+  top: 5px !important;
+}
+
+.jp-Toolbar-responsive-popup {
+  position: absolute;
+  height: fit-content;
+  display: flex;
+  flex-direction: row;
+  flex-wrap: wrap;
+  justify-content: flex-end;
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  background: var(--jp-toolbar-background);
+  min-height: var(--jp-toolbar-micro-height);
+  padding: var(--jp-notebook-toolbar-padding);
+  z-index: 1;
+  right: 0;
+  top: 0;
+}
+
+.jp-Toolbar > .jp-Toolbar-responsive-opener {
+  margin-left: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-ExecutionIndicator {
+  position: relative;
+  display: inline-block;
+  height: 100%;
+  z-index: 9997;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip {
+  visibility: hidden;
+  height: auto;
+  width: max-content;
+  width: -moz-max-content;
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color1);
+  text-align: justify;
+  border-radius: 6px;
+  padding: 0 5px;
+  position: fixed;
+  display: table;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.up {
+  transform: translateX(-50%) translateY(-100%) translateY(-32px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.down {
+  transform: translateX(calc(-100% + 16px)) translateY(5px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.hidden {
+  display: none;
+}
+
+.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
+  visibility: visible;
+}
+
+.jp-Notebook-ExecutionIndicator span {
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  color: var(--jp-ui-font-color1);
+  line-height: 24px;
+  display: block;
+}
+
+.jp-Notebook-ExecutionIndicator-progress-bar {
+  display: flex;
+  justify-content: center;
+  height: 100%;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*
+ * Execution indicator
+ */
+.jp-tocItem-content::after {
+  content: '';
+
+  /* Must be identical to form a circle */
+  width: 12px;
+  height: 12px;
+  background: none;
+  border: none;
+  position: absolute;
+  right: 0;
+}
+
+.jp-tocItem-content[data-running='0']::after {
+  border-radius: 50%;
+  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+  background: none;
+}
+
+.jp-tocItem-content[data-running='1']::after {
+  border-radius: 50%;
+  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+  background-color: var(--jp-inverse-layout-color3);
+}
+
+.jp-tocItem-content[data-running='0'],
+.jp-tocItem-content[data-running='1'] {
+  margin-right: 12px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Notebook-footer {
+  height: 27px;
+  margin-left: calc(
+    var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+      var(--jp-cell-padding)
+  );
+  width: calc(
+    100% -
+      (
+        var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+          var(--jp-cell-padding) + var(--jp-cell-padding)
+      )
+  );
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  color: var(--jp-ui-font-color3);
+  margin-top: 6px;
+  background: none;
+  cursor: pointer;
+}
+
+.jp-Notebook-footer:focus {
+  border-color: var(--jp-cell-editor-active-border-color);
+}
+
+/* For devices that support hovering, hide footer until hover */
+@media (hover: hover) {
+  .jp-Notebook-footer {
+    opacity: 0;
+  }
+
+  .jp-Notebook-footer:focus,
+  .jp-Notebook-footer:hover {
+    opacity: 1;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Imports
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-side-by-side-output-size: 1fr;
+  --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
+  --jp-private-notebook-dragImage-width: 304px;
+  --jp-private-notebook-dragImage-height: 36px;
+  --jp-private-notebook-selected-color: var(--md-blue-400);
+  --jp-private-notebook-active-color: var(--md-green-400);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook
+|----------------------------------------------------------------------------*/
+
+/* stylelint-disable selector-max-class */
+
+.jp-NotebookPanel {
+  display: block;
+  height: 100%;
+}
+
+.jp-NotebookPanel.jp-Document {
+  min-width: 240px;
+  min-height: 120px;
+}
+
+.jp-Notebook {
+  padding: var(--jp-notebook-padding);
+  outline: none;
+  overflow: auto;
+  background: var(--jp-layout-color0);
+}
+
+.jp-Notebook.jp-mod-scrollPastEnd::after {
+  display: block;
+  content: '';
+  min-height: var(--jp-notebook-scroll-padding);
+}
+
+.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
+  contain: strict;
+}
+
+.jp-Notebook .jp-Cell {
+  overflow: visible;
+}
+
+.jp-Notebook .jp-Cell .jp-InputPrompt {
+  cursor: move;
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook state related styling
+|
+| The notebook and cells each have states, here are the possibilities:
+|
+| - Notebook
+|   - Command
+|   - Edit
+| - Cell
+|   - None
+|   - Active (only one can be active)
+|   - Selected (the cells actions are applied to)
+|   - Multiselected (when multiple selected, the cursor)
+|   - No outputs
+|----------------------------------------------------------------------------*/
+
+/* Command or edit modes */
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+/* cell is active */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
+  background: var(--jp-brand-color1);
+}
+
+/* cell is dirty */
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
+  color: var(--jp-warn-color1);
+}
+
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
+  color: var(--jp-warn-color1);
+  content: '•';
+}
+
+.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
+  background: var(--jp-warn-color1);
+}
+
+/* collapser is hovered */
+.jp-Notebook .jp-Cell .jp-Collapser:hover {
+  box-shadow: var(--jp-elevation-z2);
+  background: var(--jp-brand-color1);
+  opacity: var(--jp-cell-collapser-not-active-hover-opacity);
+}
+
+/* cell is active and collapser is hovered */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
+  background: var(--jp-brand-color0);
+  opacity: 1;
+}
+
+/* Command mode */
+
+.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
+  background: var(--jp-notebook-multiselected-color);
+}
+
+.jp-Notebook.jp-mod-commandMode
+  .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
+  background: transparent;
+}
+
+/* Edit mode */
+
+.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
+  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-cell-editor-active-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook drag and drop
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-cell.jp-mod-dropSource {
+  opacity: 0.5;
+}
+
+.jp-Notebook-cell.jp-mod-dropTarget,
+.jp-Notebook.jp-mod-commandMode
+  .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
+  border-top-color: var(--jp-private-notebook-selected-color);
+  border-top-style: solid;
+  border-top-width: 2px;
+}
+
+.jp-dragImage {
+  display: block;
+  flex-direction: row;
+  width: var(--jp-private-notebook-dragImage-width);
+  height: var(--jp-private-notebook-dragImage-height);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+  overflow: visible;
+}
+
+.jp-dragImage-singlePrompt {
+  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
+}
+
+.jp-dragImage .jp-dragImage-content {
+  flex: 1 1 auto;
+  z-index: 2;
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  line-height: var(--jp-code-line-height);
+  padding: var(--jp-code-padding);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background-color);
+  color: var(--jp-content-font-color3);
+  text-align: left;
+  margin: 4px 4px 4px 0;
+}
+
+.jp-dragImage .jp-dragImage-prompt {
+  flex: 0 0 auto;
+  min-width: 36px;
+  color: var(--jp-cell-inprompt-font-color);
+  padding: var(--jp-code-padding);
+  padding-left: 12px;
+  font-family: var(--jp-cell-prompt-font-family);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: 1.9;
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+}
+
+.jp-dragImage-multipleBack {
+  z-index: -1;
+  position: absolute;
+  height: 32px;
+  width: 300px;
+  top: 8px;
+  left: 8px;
+  background: var(--jp-layout-color2);
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
+}
+
+/*-----------------------------------------------------------------------------
+| Cell toolbar
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookTools {
+  display: block;
+  min-width: var(--jp-sidebar-min-width);
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+
+  /* This is needed so that all font sizing of children done in ems is
+    * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  overflow: auto;
+}
+
+.jp-ActiveCellTool {
+  padding: 12px 0;
+  display: flex;
+}
+
+.jp-ActiveCellTool-Content {
+  flex: 1 1 auto;
+}
+
+.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
+  background: var(--jp-cell-editor-background);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  border-radius: 0;
+  min-height: 29px;
+}
+
+.jp-ActiveCellTool .jp-InputPrompt {
+  min-width: calc(var(--jp-cell-prompt-width) * 0.75);
+}
+
+.jp-ActiveCellTool-CellContent > pre {
+  padding: 5px 4px;
+  margin: 0;
+  white-space: normal;
+}
+
+.jp-MetadataEditorTool {
+  flex-direction: column;
+  padding: 12px 0;
+}
+
+.jp-RankedPanel > :not(:first-child) {
+  margin-top: 12px;
+}
+
+.jp-KeySelector select.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+}
+
+.jp-KeySelector label,
+.jp-MetadataEditorTool label,
+.jp-NumberSetter label {
+  line-height: 1.4;
+}
+
+.jp-NotebookTools .jp-select-wrapper {
+  margin-top: 4px;
+  margin-bottom: 0;
+}
+
+.jp-NumberSetter input {
+  width: 100%;
+  margin-top: 4px;
+}
+
+.jp-NotebookTools .jp-Collapse {
+  margin-top: 16px;
+}
+
+/*-----------------------------------------------------------------------------
+| Presentation Mode (.jp-mod-presentationMode)
+|----------------------------------------------------------------------------*/
+
+.jp-mod-presentationMode .jp-Notebook {
+  --jp-content-font-size1: var(--jp-content-presentation-font-size1);
+  --jp-code-font-size: var(--jp-code-presentation-font-size);
+}
+
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
+  flex: 0 0 110px;
+}
+
+/*-----------------------------------------------------------------------------
+| Side-by-side Mode (.jp-mod-sideBySide)
+|----------------------------------------------------------------------------*/
+.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
+  margin-top: 3em;
+  margin-bottom: 3em;
+  margin-left: 5%;
+  margin-right: 5%;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) min-content minmax(
+      0,
+      var(--jp-side-by-side-output-size)
+    );
+  grid-template-rows: auto minmax(0, 1fr) auto;
+  grid-template-areas:
+    'header header header'
+    'input handle output'
+    'footer footer footer';
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
+  grid-template-columns: minmax(0, 1fr) min-content minmax(
+      0,
+      var(--jp-side-by-side-resized-cell)
+    );
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
+  grid-area: header;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
+  grid-area: input;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
+  /* overwrite the default margin (no vertical separation needed in side by side move */
+  margin-top: 0;
+  grid-area: output;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
+  grid-area: footer;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
+  grid-area: handle;
+  user-select: none;
+  display: block;
+  height: 100%;
+  cursor: ew-resize;
+  padding: 0 var(--jp-cell-padding);
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
+  content: '';
+  display: block;
+  background: var(--jp-border-color2);
+  height: 100%;
+  width: 5px;
+}
+
+.jp-mod-sideBySide.jp-Notebook
+  .jp-CodeCell.jp-mod-resizedCell
+  .jp-CellResizeHandle::after {
+  background: var(--jp-border-color0);
+}
+
+.jp-CellResizeHandle {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-Placeholder {
+  padding-left: 55px;
+}
+
+.jp-Cell-Placeholder-wrapper {
+  background: #fff;
+  border: 1px solid;
+  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
+  border-radius: 4px;
+  -webkit-border-radius: 4px;
+  margin: 10px 15px;
+}
+
+.jp-Cell-Placeholder-wrapper-inner {
+  padding: 15px;
+  position: relative;
+}
+
+.jp-Cell-Placeholder-wrapper-body {
+  background-repeat: repeat;
+  background-size: 50% auto;
+}
+
+.jp-Cell-Placeholder-wrapper-body div {
+  background: #f6f7f8;
+  background-image: -webkit-linear-gradient(
+    left,
+    #f6f7f8 0%,
+    #edeef1 20%,
+    #f6f7f8 40%,
+    #f6f7f8 100%
+  );
+  background-repeat: no-repeat;
+  background-size: 800px 104px;
+  height: 104px;
+  position: absolute;
+  right: 15px;
+  left: 15px;
+  top: 15px;
+}
+
+div.jp-Cell-Placeholder-h1 {
+  top: 20px;
+  height: 20px;
+  left: 15px;
+  width: 150px;
+}
+
+div.jp-Cell-Placeholder-h2 {
+  left: 15px;
+  top: 50px;
+  height: 10px;
+  width: 100px;
+}
+
+div.jp-Cell-Placeholder-content-1,
+div.jp-Cell-Placeholder-content-2,
+div.jp-Cell-Placeholder-content-3 {
+  left: 15px;
+  right: 15px;
+  height: 10px;
+}
+
+div.jp-Cell-Placeholder-content-1 {
+  top: 100px;
+}
+
+div.jp-Cell-Placeholder-content-2 {
+  top: 120px;
+}
+
+div.jp-Cell-Placeholder-content-3 {
+  top: 140px;
+}
+
+</style>
+<style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
+    0 1px 1px 0 var(--jp-shadow-penumbra-color),
+    0 1px 3px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
+    0 2px 2px 0 var(--jp-shadow-penumbra-color),
+    0 1px 5px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
+    0 4px 5px 0 var(--jp-shadow-penumbra-color),
+    0 1px 10px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
+    0 6px 10px 0 var(--jp-shadow-penumbra-color),
+    0 1px 18px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
+    0 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
+    0 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
+    0 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
+    0 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
+    0 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-inverse-border-color: var(--md-grey-600);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+  --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
+    helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+  --jp-content-link-color: var(--md-blue-900);
+  --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
+    'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
+    'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
+  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+  --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
+  --jp-code-font-family: var(--jp-code-font-family-default);
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-code-presentation-font-size: 16px;
+
+  /* may need to tweak cursor width if you change font size */
+  --jp-code-cursor-width0: 1.4px;
+  --jp-code-cursor-width1: 2px;
+  --jp-code-cursor-width2: 4px;
+
+  /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+  --jp-layout-color0: white;
+  --jp-layout-color1: white;
+  --jp-layout-color2: var(--md-grey-200);
+  --jp-layout-color3: var(--md-grey-400);
+  --jp-layout-color4: var(--md-grey-600);
+
+  /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+  --jp-inverse-layout-color0: #111;
+  --jp-inverse-layout-color1: var(--md-grey-900);
+  --jp-inverse-layout-color2: var(--md-grey-800);
+  --jp-inverse-layout-color3: var(--md-grey-700);
+  --jp-inverse-layout-color4: var(--md-grey-600);
+
+  /* Brand/accent */
+
+  --jp-brand-color0: var(--md-blue-900);
+  --jp-brand-color1: var(--md-blue-700);
+  --jp-brand-color2: var(--md-blue-300);
+  --jp-brand-color3: var(--md-blue-100);
+  --jp-brand-color4: var(--md-blue-50);
+  --jp-accent-color0: var(--md-green-900);
+  --jp-accent-color1: var(--md-green-700);
+  --jp-accent-color2: var(--md-green-300);
+  --jp-accent-color3: var(--md-green-100);
+
+  /* State colors (warn, error, success, info) */
+
+  --jp-warn-color0: var(--md-orange-900);
+  --jp-warn-color1: var(--md-orange-700);
+  --jp-warn-color2: var(--md-orange-300);
+  --jp-warn-color3: var(--md-orange-100);
+  --jp-error-color0: var(--md-red-900);
+  --jp-error-color1: var(--md-red-700);
+  --jp-error-color2: var(--md-red-300);
+  --jp-error-color3: var(--md-red-100);
+  --jp-success-color0: var(--md-green-900);
+  --jp-success-color1: var(--md-green-700);
+  --jp-success-color2: var(--md-green-300);
+  --jp-success-color3: var(--md-green-100);
+  --jp-info-color0: var(--md-cyan-900);
+  --jp-info-color1: var(--md-cyan-700);
+  --jp-info-color2: var(--md-cyan-300);
+  --jp-info-color3: var(--md-cyan-100);
+
+  /* Cell specific styles */
+
+  --jp-cell-padding: 5px;
+  --jp-cell-collapser-width: 8px;
+  --jp-cell-collapser-min-height: 20px;
+  --jp-cell-collapser-not-active-hover-opacity: 0.6;
+  --jp-cell-editor-background: var(--md-grey-100);
+  --jp-cell-editor-border-color: var(--md-grey-300);
+  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-cell-editor-active-background: var(--jp-layout-color0);
+  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+  --jp-cell-prompt-width: 64px;
+  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+  --jp-cell-prompt-letter-spacing: 0;
+  --jp-cell-prompt-opacity: 1;
+  --jp-cell-prompt-not-active-opacity: 0.5;
+  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+
+  /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+  --jp-cell-inprompt-font-color: #307fc1;
+
+  /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+  --jp-cell-outprompt-font-color: #bf5b3d;
+
+  /* Notebook specific styles */
+
+  --jp-notebook-padding: 10px;
+  --jp-notebook-select-background: var(--jp-layout-color1);
+  --jp-notebook-multiselected-color: var(--md-blue-50);
+
+  /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+  --jp-notebook-scroll-padding: calc(
+    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+  );
+
+  /* Rendermime styles */
+
+  --jp-rendermime-error-background: #fdd;
+  --jp-rendermime-table-row-background: var(--md-grey-100);
+  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+  /* Dialog specific styles */
+
+  --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+  /* Console specific styles */
+
+  --jp-console-padding: 10px;
+
+  /* Toolbar specific styles */
+
+  --jp-toolbar-border-color: var(--jp-border-color1);
+  --jp-toolbar-micro-height: 8px;
+  --jp-toolbar-background: var(--jp-layout-color1);
+  --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
+  --jp-toolbar-header-margin: 4px 4px 0 4px;
+  --jp-toolbar-active-background: var(--md-grey-300);
+
+  /* Statusbar specific styles */
+
+  --jp-statusbar-height: 24px;
+
+  /* Input field styles */
+
+  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-input-active-background: var(--jp-layout-color1);
+  --jp-input-hover-background: var(--jp-layout-color1);
+  --jp-input-background: var(--md-grey-100);
+  --jp-input-border-color: var(--jp-inverse-border-color);
+  --jp-input-active-border-color: var(--jp-brand-color1);
+  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+  /* General editor styles */
+
+  --jp-editor-selected-background: #d9d9d9;
+  --jp-editor-selected-focused-background: #d7d4f0;
+  --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+  /* Code mirror specific styles */
+
+  --jp-mirror-editor-keyword-color: #008000;
+  --jp-mirror-editor-atom-color: #88f;
+  --jp-mirror-editor-number-color: #080;
+  --jp-mirror-editor-def-color: #00f;
+  --jp-mirror-editor-variable-color: var(--md-grey-900);
+  --jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
+  --jp-mirror-editor-variable-3-color: #085;
+  --jp-mirror-editor-punctuation-color: #05a;
+  --jp-mirror-editor-property-color: #05a;
+  --jp-mirror-editor-operator-color: #a2f;
+  --jp-mirror-editor-comment-color: #408080;
+  --jp-mirror-editor-string-color: #ba2121;
+  --jp-mirror-editor-string-2-color: #708;
+  --jp-mirror-editor-meta-color: #a2f;
+  --jp-mirror-editor-qualifier-color: #555;
+  --jp-mirror-editor-builtin-color: #008000;
+  --jp-mirror-editor-bracket-color: #997;
+  --jp-mirror-editor-tag-color: #170;
+  --jp-mirror-editor-attribute-color: #00c;
+  --jp-mirror-editor-header-color: blue;
+  --jp-mirror-editor-quote-color: #090;
+  --jp-mirror-editor-link-color: #00c;
+  --jp-mirror-editor-error-color: #f00;
+  --jp-mirror-editor-hr-color: #999;
+
+  /*
+    RTC user specific colors.
+    These colors are used for the cursor, username in the editor,
+    and the icon of the user.
+  */
+
+  --jp-collaborator-color1: #ffad8e;
+  --jp-collaborator-color2: #dac83d;
+  --jp-collaborator-color3: #72dd76;
+  --jp-collaborator-color4: #00e4d0;
+  --jp-collaborator-color5: #45d4ff;
+  --jp-collaborator-color6: #e2b1ff;
+  --jp-collaborator-color7: #ff9de6;
+
+  /* Vega extension styles */
+
+  --jp-vega-background: white;
+
+  /* Sidebar-related styles */
+
+  --jp-sidebar-min-width: 250px;
+
+  /* Search-related styles */
+
+  --jp-search-toggle-off-opacity: 0.5;
+  --jp-search-toggle-hover-opacity: 0.8;
+  --jp-search-toggle-on-opacity: 1;
+  --jp-search-selected-match-background-color: rgb(245, 200, 0);
+  --jp-search-selected-match-color: black;
+  --jp-search-unselected-match-background-color: var(
+    --jp-inverse-layout-color0
+  );
+  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+  /* Icon colors that work well with light or dark backgrounds */
+  --jp-icon-contrast-color0: var(--md-purple-600);
+  --jp-icon-contrast-color1: var(--md-green-600);
+  --jp-icon-contrast-color2: var(--md-pink-600);
+  --jp-icon-contrast-color3: var(--md-blue-600);
+
+  /* Button colors */
+  --jp-accept-color-normal: var(--md-blue-700);
+  --jp-accept-color-hover: var(--md-blue-800);
+  --jp-accept-color-active: var(--md-blue-900);
+  --jp-warn-color-normal: var(--md-red-700);
+  --jp-warn-color-hover: var(--md-red-800);
+  --jp-warn-color-active: var(--md-red-900);
+  --jp-reject-color-normal: var(--md-grey-600);
+  --jp-reject-color-hover: var(--md-grey-700);
+  --jp-reject-color-active: var(--md-grey-800);
+
+  /* File or activity icons and switch semantic variables */
+  --jp-jupyter-icon-color: #f37626;
+  --jp-notebook-icon-color: #f37626;
+  --jp-json-icon-color: var(--md-orange-700);
+  --jp-console-icon-background-color: var(--md-blue-700);
+  --jp-console-icon-color: white;
+  --jp-terminal-icon-background-color: var(--md-grey-800);
+  --jp-terminal-icon-color: var(--md-grey-200);
+  --jp-text-editor-icon-color: var(--md-grey-700);
+  --jp-inspector-icon-color: var(--md-grey-700);
+  --jp-switch-color: var(--md-grey-400);
+  --jp-switch-true-position-color: var(--md-orange-900);
+}
+</style>
+<style type="text/css">
+/* Force rendering true colors when outputing to pdf */
+* {
+  -webkit-print-color-adjust: exact;
+}
+
+/* Misc */
+a.anchor-link {
+  display: none;
+}
+
+/* Input area styling */
+.jp-InputArea {
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  overflow: hidden;
+}
+
+.cm-editor.cm-s-jupyter .highlight pre {
+/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
+  padding: var(--jp-code-padding) 4px;
+  margin: 0;
+
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+  color: inherit;
+
+}
+
+.jp-OutputArea-output pre {
+  line-height: inherit;
+  font-family: inherit;
+}
+
+.jp-RenderedText pre {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-code-font-size);
+}
+
+/* Hiding the collapser by default */
+.jp-Collapser {
+  display: none;
+}
+
+@page {
+    margin: 0.5in; /* Margin for each printed piece of paper */
+}
+
+@media print {
+  .jp-Cell-inputWrapper,
+  .jp-Cell-outputWrapper {
+    display: block;
+  }
+}
+</style>
+<!-- Load mathjax -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+<!-- MathJax configuration -->
+<script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+<!-- End of mathjax configuration --><script type="module">
+  document.addEventListener("DOMContentLoaded", async () => {
+    const diagrams = document.querySelectorAll(".jp-Mermaid > pre.mermaid");
+    // do not load mermaidjs if not needed
+    if (!diagrams.length) {
+      return;
+    }
+    const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
+    const parser = new DOMParser();
+
+    mermaid.initialize({
+      maxTextSize: 100000,
+      maxEdges: 100000,
+      startOnLoad: false,
+      fontFamily: window
+        .getComputedStyle(document.body)
+        .getPropertyValue("--jp-ui-font-family"),
+      theme: document.querySelector("body[data-jp-theme-light='true']")
+        ? "default"
+        : "dark",
+    });
+
+    let _nextMermaidId = 0;
+
+    function makeMermaidImage(svg) {
+      const img = document.createElement("img");
+      const doc = parser.parseFromString(svg, "image/svg+xml");
+      const svgEl = doc.querySelector("svg");
+      const { maxWidth } = svgEl?.style || {};
+      const firstTitle = doc.querySelector("title");
+      const firstDesc = doc.querySelector("desc");
+
+      img.setAttribute("src", `data:image/svg+xml,${encodeURIComponent(svg)}`);
+      if (maxWidth) {
+        img.width = parseInt(maxWidth);
+      }
+      if (firstTitle) {
+        img.setAttribute("alt", firstTitle.textContent);
+      }
+      if (firstDesc) {
+        const caption = document.createElement("figcaption");
+        caption.className = "sr-only";
+        caption.textContent = firstDesc.textContent;
+        return [img, caption];
+      }
+      return [img];
+    }
+
+    async function makeMermaidError(text) {
+      let errorMessage = "";
+      try {
+        await mermaid.parse(text);
+      } catch (err) {
+        errorMessage = `${err}`;
+      }
+
+      const result = document.createElement("details");
+      result.className = 'jp-RenderedMermaid-Details';
+      const summary = document.createElement("summary");
+      summary.className = 'jp-RenderedMermaid-Summary';
+      const pre = document.createElement("pre");
+      const code = document.createElement("code");
+      code.innerText = text;
+      pre.appendChild(code);
+      summary.appendChild(pre);
+      result.appendChild(summary);
+
+      const warning = document.createElement("pre");
+      warning.innerText = errorMessage;
+      result.appendChild(warning);
+      return [result];
+    }
+
+    async function renderOneMarmaid(src) {
+      const id = `jp-mermaid-${_nextMermaidId++}`;
+      const parent = src.parentNode;
+      let raw = src.textContent.trim();
+      const el = document.createElement("div");
+      el.style.visibility = "hidden";
+      document.body.appendChild(el);
+      let results = null;
+      let output = null;
+      try {
+        let { svg } = await mermaid.render(id, raw, el);
+        svg = cleanMermaidSvg(svg);
+        results = makeMermaidImage(svg);
+        output = document.createElement("figure");
+        results.map(output.appendChild, output);
+      } catch (err) {
+        parent.classList.add("jp-mod-warning");
+        results = await makeMermaidError(raw);
+        output = results[0];
+      } finally {
+        el.remove();
+      }
+      parent.classList.add("jp-RenderedMermaid");
+      parent.appendChild(output);
+    }
+
+
+    /**
+     * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+     */
+    function cleanMermaidSvg(svg) {
+      return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+    }
+
+
+    /**
+     * A regular expression for all void elements, which may include attributes and
+     * a slash.
+     *
+     * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+     *
+     * Of these, only `<br>` is generated by Mermaid in place of `\n`,
+     * but _any_ "malformed" tag will break the SVG rendering entirely.
+     */
+    const RE_VOID_ELEMENT =
+      /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+    /**
+     * Ensure a void element is closed with a slash, preserving any attributes.
+     */
+    function replaceVoidElement(match, tag, rest) {
+      rest = rest.trim();
+      if (!rest.endsWith('/')) {
+        rest = `${rest} /`;
+      }
+      return `<${tag} ${rest}>`;
+    }
+
+    void Promise.all([...diagrams].map(renderOneMarmaid));
+  });
+</script>
+<style>
+  .jp-Mermaid:not(.jp-RenderedMermaid) {
+    display: none;
+  }
+
+  .jp-RenderedMermaid {
+    overflow: auto;
+    display: flex;
+  }
+
+  .jp-RenderedMermaid.jp-mod-warning {
+    width: auto;
+    padding: 0.5em;
+    margin-top: 0.5em;
+    border: var(--jp-border-width) solid var(--jp-warn-color2);
+    border-radius: var(--jp-border-radius);
+    color: var(--jp-ui-font-color1);
+    font-size: var(--jp-ui-font-size1);
+    white-space: pre-wrap;
+    word-wrap: break-word;
+  }
+
+  .jp-RenderedMermaid figure {
+    margin: 0;
+    overflow: auto;
+    max-width: 100%;
+  }
+
+  .jp-RenderedMermaid img {
+    max-width: 100%;
+  }
+
+  .jp-RenderedMermaid-Details > pre {
+    margin-top: 1em;
+  }
+
+  .jp-RenderedMermaid-Summary {
+    color: var(--jp-warn-color2);
+  }
+
+  .jp-RenderedMermaid:not(.jp-mod-warning) pre {
+    display: none;
+  }
+
+  .jp-RenderedMermaid-Summary > pre {
+    display: inline-block;
+    white-space: normal;
+  }
+</style>
+<!-- End of mermaid configuration --></head>
+<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
+<main><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">lambeq.backend</span> <span class="kn">import</span> <span class="n">Ty</span><span class="p">,</span> <span class="n">Box</span><span class="p">,</span> <span class="n">Word</span><span class="p">,</span><span class="n">Frame</span><span class="p">,</span> <span class="n">Swap</span>
+<span class="kn">from</span> <span class="nn">lambeq_discocirc</span> <span class="kn">import</span> <span class="n">DisCoCircReader</span>
+<span class="n">s</span> <span class="o">=</span> <span class="n">Ty</span><span class="p">(</span><span class="s1">'s'</span><span class="p">)</span>
+<span class="n">n</span> <span class="o">=</span> <span class="n">Ty</span><span class="p">(</span><span class="s1">'n'</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">file_path</span> <span class="o">=</span> <span class="s1">'train-00.txt'</span>
+<span class="n">reader</span> <span class="o">=</span> <span class="n">DisCoCircReader</span><span class="p">()</span>
+<span class="c1"># Open the file using a context manager</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+    <span class="c1"># Iterate through each line in the file</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' sample -&gt; </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">, </span><span class="se">\n</span><span class="s1"> </span><span class="si">{</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+            <span class="n">diagram</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">text2circuit</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
+            <span class="n">diagram</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">foliated</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">draw_type_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">color_boxes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">color_wire</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="c1"># Skip lines that cause exceptions and print an error message</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Skipping line </span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> due to an error: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 0, 
+ Me no sabby no way insisted Chow . Oh yes you do snapped Dave . But you wo n't tell .
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 1, 
+ No replied Darrin with great promptness . You 're going to do that yourself . What gasped Midshipman Pennington in intense astonishment .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 2, 
+ In what way shall I do it Hallam . Ca n't you suggest something . Yes retorted Hallam with great energy .
+Skipping line 3 due to an error: Bobcat failed to parse "Ca n't you suggest something .".
+ sample -&gt; 3, 
+ What was that asked Dr Mackenzie glancing at him sharply . Did you try the aid of liquor . Worse I 'm afraid sir .
+Skipping line 4 due to an error: Bobcat failed to parse "Worse I ' m afraid sir .".
+ sample -&gt; 4, 
+ Besides it 's a serious breach of discipline . I shall have to report you Mr Pennington . I expected it sir replied Pennington meekly .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 5, 
+ Unfortunately you are quite right smiled Dave . Still I 'd do the same thing over again . Oh of course you would assented Dan .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 6, 
+ That 's because you 're Dave Darrin . Here a voice like a bass horn was heard . All third classmen report to the quarter deck immediately .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 7, 
+ Then his attention began to wander . A brilliantly shining brass rod near him caught his eye . Something about the glossy metal fascinated him .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 8, 
+ Then point him out to me insisted the officer . There was hardly any need to do so . Pennington 's face flushed with mortification was sufficient identification .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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jEAAAAAAACEFQIxAAAAAAAAhBUCMQAAAAAAAIQVAjEAAAAAAACEFQIxAAAaQfUOv0zTtLoMhIFAIKAdVVVWlwEAANCiRVhdAAAALZ1rSHt9PaZA75z+mY46ua2SjklQjDNasakxik2NVkxqjKJTomSzG1aXihbCHwhow/YylW7bql/Lt6p021aVlm3Vj+tK9flKjzb5tutPZ51vdZkAAAAtFoEYAABHqEPOURr03Mn69dsNWj9/o0rnbVDllvpn8Bh2QzFtohSTGqOopEjZo22yR9lki7LLHmWTPXrP7trXSHvNtHXDouzBcfY9+6Nru6NsMiIMGQYBXGMxTVNVfr92+qu1o6pqv687qqu0s7r6gK8HGrfRV65fy7ZpXfk2BfY449AZF68ubVJ1w4mn6qxj3Dr96K4W7Q0AAICWj0AMAIAjZIuwKa2/U2n9ncFh/sqAdmzcqR0bdqhiw07t8O5URW135bYqBSoDqtruV2Bzpfw7A/JXBhSorH3d6a8ZVlXTbQYaUJSh+iFblE226JoAzR5lky3SJhk10xk2o+bVqHuVZDNqRteO85sBVZsBVQX8qjL9Na9+v6oVkCnJNMyaV5kyjZoAKbDbsIBhyjRVO6zmv4BMbei4UytMrwK1tciomUbGrmXJUM0y69ZjSJJZM48k066aADDCkGq7Za/ZBlOmAmZNHQEzINOsrSdg7hpXO6zSX60dVdXa6a/a+7W6ukGXxNoMQzERkYqJjFS0PWLX6z6GpcQ61KVtqtLiE5WemKT0hCSlJyQrPTFJ7eISFBXBn20AAACNhb+sAABoAvYom+LSYxWXHnvEywpUB+TfGVCgKiB/bVgWDM8qa4dVBhTYGZC/cvfumv6aaeqWUTP/zh1V2rR9u7ZU+FTp96uyqrom4KquCbp27672B6SajEqSZKtJpHa9ypBNhox6rzXja/5TcLghQzZTweG/xGxSYVRJzfJNQ0Zt5mTU9suUbDVJWM266sLBQP3p96ypbp22CJtsETZF1L1G1rxGRdpkj7TLHmlTdGKkopOiFBMdpZjImrAqOmLfr4c7LsJuP+L3HwAAAI2PQAwAgBBXF+ocqcL/rdFTX3+u70pWaemGdVKiFJliV5vYOCXHxiopJlbJMQ4lxzqUHBOr5Nj42v6a4UkxsbuNq3mNjYyy9NJM0zTlr/CrsrxaVduqVFlWVftaraqyKlVuq1JVeXXNa1mVKrfsmq6yrEpVW6sUqK5J+xKPjlfbXsnqNryzUronWrZNAAAAaHoEYgAAhIF3fyzSJf/8m7Kc7TQ461j9X855OqFjprqnpslua7kPnTYMQxGOCEU4IqR2MYc9f6A6oG2ryrVp8VZt+nGLSudu0Kp3SzTw2X5qf3q7JqgYAAAAoYBADACAMPDdL6t0fHonfXfLeC7j240twqbkrEQlZyWq86WdFKgK6IuR/9Gaj/5HIAYAANCKtdx/EgYAAIesvHKnqgJ+wrCDsEXalH5aO635YK02L91mdTkAAABoIgRiAAAAu+l+3dFKcMXps2vm6ee3f2nQ0yUBAAAQ2gjEAAAAdmOPtuuc105Vp8HtNf+ehZr3xwJ5F24mGAMAAGhFuIcYAADAHiLjInTyQ73V/rRUFf51iT4dNk9xHR3KGNJemed3UFJWgtUlAgAA4AgQiAEAAOyH69z26nh2ujYUbNSaD9Zq+atrtGTaCiV1TVDm+R2UNsCppKwE2aO4NxsAAEBLQiAGAABwADa7oaP6OXVUP6f63nOsSr/eoDUfrNXiZ5dp4eM/yYgwlHRMgtr0SFJKjyS16ZGk5K4JinDwZxYAAECo4i81AACAQ2SPsqvjGWnqeEaaqiv82rJsmzYv2apNS7Zq85KtWv1eiQLVpgyblHB0vFLcSUrplqi4DrFypMcqrn2sYtpGy7AZVm8KAABAWCMQAwAAaICIWLucvVPk7J0SHOav9GvrinJtXrJVmz01Qdn/Pv9V1RX+4DS2CEOO9NjgT1x6rBzpMbWvsXKkxSoyjj/RAAAAmhJ/bQEAADQSe5RdbWovm6xjmqYqt1bJV1qh7aUV8v26I9hdXrxd6+d7VbF+h8zAruVExEUoOilSUUmRikqKUlRSZG1/VO2wSEUnRSkqebdpEiMVEcO9zAAAAA4FgRgAAEATMgxD0clRik6OUoo7aZ/TBKoCqli/Q9tLK7R9bYV2bNypyq1VqtxWVfO6tVLbS3zaWdtdVVa9z+XYo22KSopURFyE7NF22aNtssfYZY+y7eqPtsseY5M9ale/LdqmiBi7bFH7GF83f+2rLdquiBibbFF22SINGQaXfwIAgJaHQAwAAMBitkib4jo4FNfBcUjTB6oDqiqr1s6tlTWB2ZbK2rCspru6wi//Tr/8OwP1Xiu3VdXrD+wMqLr21b8zoEBV4OAr341hk2zRuwIzm92QbIYMQzX3SbPVBIL1u3cbp9pXW22wFuxWzXL2uazacfW666bZ1W3YJNWue1d37XT1umuntxuyR9YGf7sFhcEQMfhq32NYTbcRQTgIAEBLQiAGAADQwtgibIpOiVJ0SlSjLjfgNxWorAvR6gdq9cOz3cfVdu/wywxIZsCUGTAls65bkmnu0a3aaXZ1m6ak2nl3dddccrp7t1klmYGAzNrlq3b6mu7aaep1772uXbXt1u035a+q2U7/Dr8C1eZh7btgOLhXgLb3qy3aroi61xj73pfBJu66FJbLYAEAaBoEYgAAAJAk2eyGbLF2RcQSwgT8pgI7/fLXBYQ7AvJX1r7WDd+xx1l2+xhfL1SsDGjn1ioFdu4MDquuqFbltqqDXga773vJRSk6JZIHMgAA0AAcMQEAAIA92OyGbI4IRRzaVaxH7ICXwdYNq72n3NaVZbXDq7RzS6W028lsUUmRuz29tP4TTOPSYxWTGlNzaSsAAGGOQAwAAACwWEMvg939gQw1Ty+teYqpr7RC67/fqO1rK1S9fdfZZ0aEofhODrXpkaQUd5JSeiSpjTtRUUmNe/ktAAChjkAMAAAAaKEO5YEMlWVVtWFZhXxrK7R1Zbk2e7aq5It18lf4JUlxHR1q0yNxV0jWI0kxbaObazMAAGh2BGIAAABAKxaVEKmohEgld02sNzzgN1W2ulybl2zVpiXbtNmzVZ4XVqqqvOaMMmd2ijLP76BOg9MV04ZwDADQuhCIAQAAAGHIZjeU1CVBSV0SlHlhzTAzYKq8xCfvgs0q/mitCh7+UQUP/6i0AU5lnN9BHc9M48b9AIBWgaMZAAAAAEmSYTOU4IpTgitOR1/cUTs3V6p4dqnWfPA//WdckewxNmVdnaleY7rJHsXTSAEALReBGAAAAIB9ik6JUtbVGcq6OkPb11bo57d/0ZJpK/Trt14NePR4JR2TYHWJAAA0iM3qAgAAAACEvrj2ser5h64659VTFKgKaPYVc7Vx0WarywIAoEEIxAAAAAAcshR3kgb/6zQlZMap4M8/yjRNq0sCAOCwcckkAAAAECaKi4vl9XobZVmOwRFa94+t+v6rHxSRwP3EAEmqWrlGtjYJWrLUo4iKTVaXE9K8mzfKiPGrsLDQ6lJCXmCHX9t2bFNhWePtK6fTKZfL1WjLa4kIxAAAAIAwUFxcLLfbLZ/P17gLzmncxQGtwqWfWF1BizG+r9UVhCeHwyGPxxPWoRiBGAAAABAGvF6vfD6fZsyYIbfbbXU5AACLeDwe5ebmyuv1EogBAAAACA9ut1vZ2dlWlwEAgKW4qT4AAAAAoEV6//33lZycLL/fL0kqKiqSYRgaN25ccJobbrhBubm52rhxo4YNG6YOHTrI4XCoZ8+emjVrVr3lvfHGG+rZs6diY2PVtm1bnXXWWdq+fXuzbhOA5kEgBgAAAABokU477TSVlZVpwYIFkqQ5c+bI6XQqPz8/OM2cOXOUk5OjHTt2qG/fvvrggw+0ePFijRo1Stdee62+++47SVJpaamGDRumkSNHyuPxKD8/X0OHDuVJqkArxSWTAAAAAIAWKSkpSX369FF+fr5OOOEE5efn649//KPy8vJUXl6urVu3asWKFRo4cKA6dOigO++8Mzjv6NGjNXv2bL3++uvq16+fSktLVV1draFDhyojI0OS1LNnT6s2DUAT4wwxAAAAAECLNXDgQOXn58s0Tc2dO1dDhw6V2+3W119/rTlz5qh9+/bKysqS3+/XAw88oJ49e6pNmzaKj4/X7NmzVVxcLEnq3bu3zjzzTPXs2VNXXHGFnnvuOW3evNnirQPQVAjEAAAAAAAtVk5Ojr7++mstXLhQkZGR6t69u3JycpSfn685c+Zo4MCBkqRHH31UTz75pMaOHasvv/xSRUVFGjx4sCorKyVJdrtdn376qT766CP16NFDU6ZMUbdu3bRq1SorNw9AEyEQAwAAAAC0WHX3EXv88ceD4VddIJafn6+cnBxJ0rx583TxxRcrNzdXvXv3VufOnbVs2bJ6yzIMQ6eccory8vK0YMECRUVF6e23327uTQLQDAjEAAAAAAAtVkpKinr16qWZM2cGw6/TTz9dhYWFWrZsWTAky8rK0qeffqpvvvlGHo9HN910k9atWxdczvz58/Xwww/rhx9+UHFxsd566y1t2LBBbrfbis0C0MS4qT4AAAAAoEUbOHCgioqKgoFYmzZt1KNHD61bt07dunWTJN177736+eefNXjwYDkcDo0aNUqXXHKJtm7dKklKTEzUV199pSeeeELbtm1TRkaGJk+erPPOO8+qzQLQhAjEAAAAABxUcXGxvF6v1WU0u23btmnt2rU65phjFBHB16cDKS0t1c6dO5WZmdns6x4+fLiGDx8un8+nwsJCSdKLL74oScF+Sbrvvvt033337TV/3TQPP/zwfsc1phUrVigpKUmpqamNvuyWwOl0yuVyWV0GwhwtOgAAAIADKi4ultvtls/ns7oUAK2Aw+GQx+MhFIOlCMQAAAAAHJDX65XP59OMGTPC7n5Kn332mcaOHav8/HwlJCRYXU5Ie+CBB7RixQq9/PLLVpcS8oYMGaKLL75YN910k9WlNDuPx6Pc3Fx5vV4CMViKQAwAAADAIXG73crOzra6jGb1888/S5J69+6t5ORka4sJcU6nU6WlpWH3GWmIyMhIpaens68AC/GUSQAAAAAAAIQVAjEAAAAAAACEFQIxAAAAAAAAhBUCMQAAAAAAAIQVAjEAAAAAAACEFZ4yCQAAAKBJFRcXy+v1Wl1Gg2zevFknnniiFi9eLIfDYXU5IS0+Pl4ZGRkqLCy0upSQ16NHDxmG0aL3ldPplMvlsroMoMEIxAAAAAA0meLiYrndbvl8PqtLOSKnnXaa1SW0GK+//rrVJbQIn3zyiSZOnGh1GQ3mcDjk8XgIxdBiEYgBAAAAaDJer1c+n08zZsyQ2+22uhw0gR07dsjn86lNmzaSpKlTpyo/P1+zZs2qN13fvn01adIkDRo0yIoy0Yg8Ho9yc3Pl9XoJxNBiEYgBAAAAaHJut1vZ2dlWl4FmkJ6ertjY2H2+3126dOFzACAkcFN9AAAAAEA977//vpKTk+X3+yVJRUVFMgxD48aNC05zww03KDc3V9OnT1dycrIkafr06crLy9PChQtlGIYMw9D06dOD83i9Xl166aVyOBzKysrSe++915ybBQBBBGIAAAAAgHpOO+00lZWVacGCBZKkOXPmyOl0Kj8/PzjNnDlzlJOTU2++q666SnfccYeOPfZYlZaWqrS0VFdddVVwfF5enq688kotWrRIQ4YM0TXXXKNNmzY1xyYBQD0EYgAAAACAepKSktSnT59gAJafn68//vGPWrBggcrLy/W///1PK1as0MCBA+vNFxsbq/j4eEVERCgtLU1paWmKjY0Njh8xYoSGDRumY445Rg8//LDKy8v13XffNeemAYAkAjEAAAAAwD4MHDhQ+fn5Mk1Tc+fO1dChQ+V2u/X1119rzpw5at++vbKysg5rmb169Qp2x8XFKTExUevXr2/s0gHgoLipPgAAAABgLzk5OXrxxRe1cOFCRUZGqnv37srJyVF+fr42b96819lhhyIyMrJev2EYCgQCjVUyABwyzhADAAAAAOyl7j5ijz/+eDD8qgvE8vPz97p/WJ2oqKjgzfgBIFQRiAEAAAAA9pKSkqJevXpp5syZwfDr9NNPV2FhoZYtW7bfM8QyMzO1atUqFRUVyev1aufOnc1YNQAcGgIxAAAAAMA+DRw4UH6/PxiItWnTRj169FBaWpq6deu2z3kuu+wynXvuuRo0aJBSU1M1a9asZqwYAA4N9xADAAAAELKKi4vl9XqbbX1VVVX69NNPdfzxxys9Pb3Z1huqhg8fruHDh8vn86mwsFCS9OKLL0qS3n//fRUVFenss8/WF198ERwvSePHj9f48eOD/YWFhSooKAh21/niiy/2GnYonE6nXC5XwzbqIJr7M1deXq6vvvpKJ598stq0adNs65Wadj9apbnfv8rKSn322WfKzs5WWlpas623Tmt8D5sLgRgAAACAkFRcXCy32y2fz2d1KTiAP/3pT82+TofDIY/H0+hBQLh95ppqP1ol3N4/qfW9h82JQAwAAABASPJ6vfL5fJoxY4bcbnezrLOsrEw5OTl65JFHdOaZZzbLOluq2bNna/z48Zo7d64cDkezrdfj8Sg3N1der7fRQwArPnOrV6/WZZddpueff17HH398s6xTatr9aBUr3r+tW7fqjDPO0KOPPqozzjijWdZZpzW+h82JQAwAAABASHO73crOzm6WdW3ZskWS1Llz52ZbZ0u1bNkySVKfPn0UHx9vcTWNqzk/c3FxcZKkrl278plrJM35/m3atEmS1KVLF96/Foab6gMAAAAAACCsEIgBAAAAAAAgrBCIAQAAAAAAIKwQiAEAAAAAcACmaWrUqFFq06aNDMNQcnKyxowZ06jrmDt3bqMuT5JycnIavc7Wyufz6bLLLlNiYqIMw9CWLVv2OSwzM1NPPPHEIS1z+vTpSk5ObtK60XAEYgAAAAAQgkaMGKGJEydaXQYkffzxx5o+fbref/99lZaW6rjjjrO6JDSyl19+WXPnztU333yj0tJSJSUl7XPY999/r1GjRh3SMq+66qrgwycaS35+fjCcw5HhKZMAAAAAWqXi4mJ5vd7DmqeiokJXXXWVKisrVVhY2ESVHZqNGzcqOjra8jr2x+/366qrrtLixYsVFRV12PM7nU65XK4mqKzxrVy5Uunp6RowYIAkKSJi31+lG/KZ27hxoySprKysQe91S9qPoWzlypXq3LmzKisrtXbtWq1du1bffPONOnXqVG/YnurajJ07d+73/SspKTngunkPrUEgBgAAAKDVKS4ultvtls/na9D8r732WiNX1HDTpk2zuoQDaui+cjgc8ng8IR8EjBgxQi+//LIkyTAMZWRkKDMzs940hmFo2rRpGjNmTIM/c3/5y1/0l7/85bDnq9uPbdu21e9//3u99dZbSkhI0J133tmgOlqyzz77TJI0YMAAxcXF6fjjj9e7776r888/X3369Kl3qeMll1yi5ORkTZ8+XTk5OZozZ44kqW/fvnstd1/D9nQkbUZsbKx++uknuVwuGYah5557Th988IFmz56tDh06aPLkybrooou0evVqDRo0SJKUkpLS4PWhBoEYAAAAgFbH6/XK5/NpxowZcrvdVpfTIBMmTFD79u112mmnacSIEbr//vvVu3dvbd26VQsWLNCwYcOsLrHBPB6PcnNz5fV6Qz4Qe/LJJ9WlSxdNmzZN33//vex2u6644oq9ptu2bVu9z9zAgQN1xx136KKLLtI//vEPvfrqq3rooYeUlpamX3/9VevWrdO5554rqSZwOeqoo3Trrbfq2GOP1auvvqr33ntP77//vpKSklRWVqZLL71Ul1xyic4//3zt3LlTTz31lMrKyrRkyRJ5vV795S9/0Zw5c/Tuu++qXbt2Gj9+vAoLC9WnT59m3mPWKC0t1fjx4yVJb7zxhjIyMjR37lyZpnnQed966y3dcMMNevvtt/X000+rR48ekqQpU6Zo5cqVevTRRxUZGamkpCRdcMEF+s1vfqPf/OY3CgQCGjlypHw+n26//XZ17NhRP//8s+x2u0455RS99957mjx5cjBsW7BggW677TbdddddOv7441VSUqKJEydqw4YN9X4X8vLy9Mgjj+jRRx/VlClTdM0112jNmjXq1KmT3nzzTV122WVaunSpVq9ercGDBzfRHm39CMQAAAAAtFput1vZ2dlWl9Eg//73vyXVfFmPi4vT6NGjlZCQIEn6zW9+Y2VpYSUpKUkJCQmy2+1KS0s76PR1nzm73a7MzExlZ2dr+vTpOvbYYzVy5EgZhrHP+W688cZgoHPWWWcpPj5eO3fuVHZ2th588EGdeOKJevHFF4PTn3766erUqZOkmhvCv/DCC5oxY4bOPPNMSTX3xOrYseORbn6LUVpaKr/fL0lq3769evbsqZ49ex7SvG3atFFMTIwkqX///sE2491339WmTZt0zjnnBKeNiopSx44dlZ2drU8++UQ//vijPB6PunbtutdyFy1aJLvdHlze3XffrXvuuUf/93//F5xm06ZN+tOf/lRvvhEjRgQD74cfflhPPfWUvvvuO5177rlq06aNJKldu3YqLy8/pO3DvnFTfQAAAAAIYWeffbYyMjLUuXNnXXvttZo5c2aDL8uDNUaMGKGioiJ169ZNt956qz755JO9punVq1ewOy4uTomJiVq/fr0kaeHChfryyy8VHx8f/OnevXtw+pKSElVWVuqkk04KDmvTpo26devWhFsVWnr37q1+/fpJqgmennvuOW3evLlJ11lUVKSOHTvuMwzbl4ULF+r++++v9z4++OCDkmruRVbnQJ8FNB4CMQAAAAAIYQkJCSosLNSsWbOUnp6u++67T7179+YpcyHEMIy9Ls2rqqoKdmdnZ2vVqlV64IEHVFFRoSuvvFKXX355vekjIyP3WmYgEJAklZeX68ILL1RRUVG9n3feeadpNqgFstvt+tvf/iZJ6ty5s6ZMmaJu3bpp1apVstlsB3x/Gio2Nvawpi8vL1deXl6997Du3mPR0dHB6Q70WUDj4ZJJAAAAAK2Wx+M5rOl37typmTNnatCgQTr66KObqKqGadOmja6++mpdfPHFGjhwoJ5//nmdccYZltXz888/Kz8/X9dee+1eX+AP5nDfl1CXmpoafLqkx+NRcXGxfD6fVq9eXe/Jg1lZWcrKylLv3r01evRoffnll8Gg5ocffqh3PzW/3x+cv3379vr888+1adOmek+4rLtkrmPHjoqMjNT8+fODy9i8ebOWLVumgQMHNvn2h4q6y1F/97vf6dlnn1VGRobefvttpaamqrS0NDid3+/X4sWLgzeo393un83169ervLy83ntYWVmpkpISFRYWKiYmRiUlJcrNzdXQoUP3etjC6tWr5ff7g/N369ZN33zzjc4666zgNHXvoc12aOcr1T3Rte7yUDQcgRgAAACAVsfpdMrhcCg3N7dB8z/zzDONXFHju+uuu6wuQVLD95XD4ZDT6Wzkaqxxxhln6M0331RMTEy9z1xeXp7y8vIOOF+dhx56SA899FC98XvOv/slkXViY2Plcrl0/fXX66677lLbtm3Vrl073XPPPYccsrQG8+fP1z//+U9JNfcTW7lypTZs2CC32624uDjdfvvt+uCDD9SlSxc99thje51hGRsbK5vNts82Y8+nTE6ePFmTJ08O9s+cOVMzZ87cb227z79w4UK9++679cZHREQc8u9CRkaGDMPQ+++/H1b3iGsKBGIAAAAAWh2XyyWPxxM8a+dQlZWVKScnR4888kjw5uRWW7BggZ599lktX75cO3fulMvl0siRI+vd6NsKs2fP1vjx4zV37lw5HI7Dnt/pdIb8EyYP1eTJk/Xb3/5Wa9eulcvl0p133qnx48cHnzL51ltv6Y033lBxcbFsNpuOPfZY3XbbberevbtWr16tyy67TH/4wx80cuTI4DJ3f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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 9, 
+ I shall call you out Mr Darrin . You 'll find my hearing excellent smiled Dave . I shall make but one stipulation .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 10, 
+ What did you say particularly . That you would have to fight me . I have already signified my entire willingness Mr Pennington .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 11, 
+ With two very stiff nods the midshipmen parted . Pennington hastened at once in search of Hallam . Will you serve me old man queried Pennington .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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vKVnH766UmSKVOmZJ111sn73ve+lvHV1dWtHr+x7d7Jhz/84VRVVbVa5rnnnptly5a1XBnw7+s1cODAJHnb9RoyZEhmzZqVRx55JFdddVXLc8rlcpYvX55nnnmm5ZtR/n2+VVVVGTJkyLvmBSrvxhtvTO/evbNkyZIsX748Bx10UD7xiU/k8ssvz8iRI1s9d9GiRS3HjWTFjlPvf//7W12ZNHTo0Dz66KPvmOnfjye9evVK3759W+b75JNPZquttmr1/K233vpd17NUKqVLly750Ic+1DJs4MCBGTVqVEql0kqt08oYO3Zsdt5554wePTq77bZbdt1113ziE59It27d8pe//CXJ61frdur0+kX8S5cubXWPyGuvvTYXXXRR5syZk4ULF2bp0qVvOucMHz681b3YHn744WywwQZv+v39ux49eqzW9XwvJk6cmEsuuaTVsBkzZmTSpEktjy+77LLcc889+etf/5rFixdn0aJFK/VFDL/97W9z9tln54knnsg//vGPLF26NK+99lqam5tb5vPG737mzJlJXn+fseGGG6Z3794t8xk8eLBzGwBrzMr8DTpr1qxMmzat1XnqDXPmzHnH9wFFpRCjoqqqqrJ8+fLVNr8pU6bkS1/6Um699dZce+21mTVr1mqb979/fDF5Pfu/D3ujeFrV9Tn11FMzduzY7LfffnnggQey2Wab5Zprrmkp0t5q+eV/ux/YqnqrdXin9Vq4cGGOOuqofOlLX3rTvP79K3vfKu/q/F0Da8YbZUS3bt2y3nrrpUuXLrn22mvTuXPnPPTQQ63KrCSt3nStyHFqVY4NlTyerO5jb+fOnXPHHXfk3nvvze23354f/OAHOfHEE/Ob3/ym5TlXX3113v/+97eaJknuu+++HHzwwTnttNOy2267ZZ111sk111yTc889t9UyevXq1epxz5493zXXf36L6Oo6x6yKXr16ZcSIEa2GvVEWvuHqq6/OD3/4w5b7gB133HFZvHjxCs3/2WefzV577ZWjjz46Z555ZgYMGJB77rknhx9+eBYvXtxSiL3bef+NYc5tAKwpK/M36MKFC/Oxj30s3/3ud980n6FDh67ZoO2UQoy1Ql1dXZ5//vk8//zzLVeJPf7445k/f34222yzlufMmDEjhx56aMt0f/jDH940ry222CJbbLFFvvGNb2TMmDHveuVBW3gj+797q+zDhw9Pklx88cX5/ve/nylTpryn+6C91TI32WSTN/1BuzLGjRuXxx9//E1/rAAdw1uVEVtssUWWLVuWF198MTvssEOFkr21UaNGtdx76w0PPPBAq8ddu3Z9043P6+rqsnTp0syYMSPbbrttkmTu3Ll58sknW847a0pVVVW22267bLfddjn55JMzfPjwTJ8+PYMGDcpLL72UYcOGveUx9t57783w4cNb3SPtueeee9fljRkzJn/5y1/y1FNPdZj/OzxhwoSWK8aWL1+ep556aoV/bw899FCWL1+ec889t+VKvF/84hdrLCsAtIVx48bll7/8ZTbccMM3/Y8u3pqb6rNW2GWXXTJ69OgcfPDBmTlzZu6///4ceuihGT9+fD74wQ8mSY499tj89Kc/zZQpU/LUU0/llFNOyZ/+9KeWeTzzzDP5xje+kfvuuy/PPfdcbr/99jQ0NFRqlVp546q1c845J7Nnz84Pf/jDVh+XfPXVV3PMMce0fJvjww8/nAceeKDl44erqqGhIV/5ylfy5JNP5uqrr84PfvCDHHvsse9pnieccELuvffeli8ImD17dn7961+/6ab6QMcxcuTIHHzwwTn00ENz/fXX55lnnsn999+fs88+OzfddFNFsx111FF54okncsIJJ+Spp57KL37xizfdpH+99dbLwoULc+edd6apqSnNzc3ZZJNNsvfee+eII47IPffck1mzZmXSpElZf/31s/fee6+xvDNmzGj5cpiGhoZcf/31eemll1JXV5ejjjoqyetXPz311FN59NFHM2XKlJx33nlJkk022SQNDQ255pprMmfOnFx00UX51a9+9a7LHD9+fHbcccd8/OMfzx133JFnnnkmt9xyS6vzUHvzhz/8Iffee29KpVKOOuqoN30r9TsZMWJElixZkh/84Af585//nJ/97Gf50Y9+tAbTAsCa94UvfCHz5s3LgQcemAceeCBz5szJbbfdls985jO+EfltKMRYK1RVVeXXv/51+vfvnx133DG77LJLNt5441x77bUtzznggANy0kkn5Wtf+1q23HLLPPfcczn66KNbxldXV+eJJ57Ixz/+8YwcOTJHHnnkWvONGh/+8Idz2WWX5cILL8zYsWNz++2351vf+lbL+M6dO2fu3Lk5+eSTkyRf//rXs8cee+S00057T8s99NBD8+qrr2brrbfOF77whRx77LE58sgj39M8x4wZk7vuuitPPfVUdthhh2yxxRY5+eSTs956672n+QJrtylTpuTQQw/NV7/61YwaNSr77LNPHnjggVYfla6EjTbaKNddd12uv/76jBkzJpdccknLFVTdunVL8vp9uz73uc/lgAMOyKBBg/K9730vyevrtOWWW2avvfbKNtts0/JNj//58YTVqW/fvvn973+fPffcMyNHjsy3vvWtnHvuudljjz1argj+zW9+k9GjR2f8+PG5/PLLs9FGGyVJ/uu//itf/vKXc8wxx2TzzTfPvffem5NOOmmFlvvLX/4yW221VQ488MBsttlm+drXvtau3xxvuumm2W233TJhwoQMGTIk++yzzwpPO3bs2Jx33nn57ne/mw984AO56qqrcvbZZ6+5sADQBtZbb71Mnz49y5Yty6677prRo0fnuOOOS79+/VquiKa1qnKlbhBB4cycOTNbbrllHnrooYwbN67DLvO9WJ15J0yYkM033zwXXHBBmy4X4D+19THmzDPPzI9+9KP8+te/blfHNufJd2cbAdDROLdVjg+WAgDt2sUXX5ytttoqAwcOzPTp0/P973/fx7gBAHhHCjHa3L9/nf2K+suCBenXvXt6//PjL2tyWWuDVcn94isvpEunLhnQc2CS178N8sUXX2z5uvjVvTyAlbUqx5pX/m9uli9enD61b//tSNOnT88pp5ySf/zjHxkyZEgOPPDA/Nd//Ve7PbatbO75SxblxUXNGdm7/xpf1tpipXMvWpLODXOzbIMBSc9ivJcAoH1ZlfPNn196LX17dk5N75W73YNz2+t8ZJI209DQkLq6ujQ3N6/0tB8+6+z8+YYb8uL9M979yf+huro6pVKp4ve5WRHvZRuN+ewmebVpUWbfsGpfJNCethPQvryXY9uuGZyadM/P0/GPbau8nbYek6pP7p7y8d9bpeUWYRuNquqTO7qNz38tvicPl+ev9HLb0zYCoH15L++TqnY/L3nu7pRL7/4lO//JuU0hRhtraGhIU1PTSk/3xTt+mwPqNs32G2yw0tPW1NS0qxf5qm6jC+47O+v2GpKDxnxmlZbb3rYT0L6s6rHt/jMuy/ynn8+ul397lZbb3o5tq7KdbvjbnJzx5IN5cOIBq7TMImyjznNeyIBDLs7Llx2Rpe/v+O8lAGhfVvV90l4/eDJ7jemXz40fvNLTOrf5yCRtrLa2dpVedFW/vTO1tbUZt/nmqz/UWmZVt1GvR3pn4MCBhb4pIrD2WtVj25yBNVnSOK8wx7ZV2U4zuyxNnnzQNnoHi7s9mxeSjBo1Kt3HjVozwQBgFa3q+6Ru3f6coUOGZty4zVd/qALw3ZsAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgULpUOsDaoKGhIU1NTZWOwTsol8tpaGjIzOXLKx1lrfXKKwszN3Mzc+bMSkfhXdTU1KS2tnaVpnW8omjmzm3KK6+84tj2Dhr+9lyS2EbvoPOcFzIgyZNPPpmlXV6pdBxoU+/lfQdAR1ZVLpfLlQ5RSQ0NDamrq0tzc3Olo/AOhmyzTf7+5z/n1RdeqHSUtdbA9/fL0leX5e9/XlDpKLyL6urqlEqllX5z6nhFEQ1LdarTOU/Gse1tDR6YjBieTFeIvZ3+6Zq9O62fm5Y35qUsqnQcaFOr+r4DWPtteNR1+fSEETntwM0rHaVdKvwVYk1NTWlubs6VV16Zurq6SscBOrhSqZRJkyalqalppd+YOl4BvDdfrHQAaGPv5X0HQEdX+ELsDXV1dRk3blylYwC8K8crAACA98ZN9QEAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBiupvr4+VVVVmT9//js+b8MNN8wFF1zQJpkAKI4JEybkuOOOq3QMAIB2TSFGof3oRz9Knz59snTp0pZhCxcuTNeuXTNhwoRWz32jCBs6dGgaGxuzzjrrJEkuv/zy9OvXrw1TA1Bk119/fU4//fRKxwAAaNcUYhTaxIkTs3Dhwjz44IMtw+6+++4MGTIkM2bMyGuvvdYyfNq0aamtrc2oUaMyZMiQVFVVVSIyAAU3YMCA9OnTp9IxAADaNYUYhTZq1KgMHTo09fX1LcPq6+uz9957Z6ONNsof/vCHVsMnTpzY6iOT9fX1+cxnPpO///3vqaqqSlVVVU499dSWaZqbm3PYYYelT58+qa2tzY9//OM2XDsAOqJ//8jkhhtumLPOOsu5BgBgJSnEKLyJEydm2rRpLY+nTZuWCRMmZPz48S3DX3311cyYMSMTJ05sNe22226bCy64IH379k1jY2MaGxtz/PHHt4w/99xz88EPfjB//OMf8/nPfz5HH310nnzyybZZMQAKwbkGAGDlKcQovIkTJ2b69OlZunRpFixYkD/+8Y8ZP358dtxxx5Yrx+67774sWrToTYVYt27dss4666SqqipDhgzJkCFD0rt375bxe+65Zz7/+c9nxIgROeGEE1JTU9OqfAOA98q5BgBg5SnEKLwJEybklVdeyQMPPJC77747I0eOzKBBgzJ+/PiW+4jV19dn4403Tm1t7UrNe8yYMS0/v1Gavfjii6t7FQAoMOcaAICV16XSAaDSRowYkQ022CDTpk3Lyy+/nPHjxydJ1ltvvQwbNiz33ntvpk2blp122mml5921a9dWj6uqqrJ8+fLVkhsAEucaAIBV4QoxSFpull9fX58JEya0DN9xxx1zyy235P7773/TxyXf0K1btyxbtqyNkgIAAADvlUIM8nohds899+Thhx9uuUIsScaPH59LL700ixcvfttCbMMNN8zChQtz5513pqmpKc3NzW0VGwAAAFgFCjHI64XYq6++mhEjRmTw4MEtw8ePH58FCxZk1KhRGTp06FtOu+222+Zzn/tcDjjggAwaNCjf+9732io2AAAAsArcQwzy+lVe5XL5TcOHDx/+puETJkx407BLLrkkl1xySathzz777Jvm9/DDD7/nrAAU2xvfgJw41wAArCpXiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBC6VLpAO1ZQ0NDmpqaKh2jQ5o9e3aWLFmSzTbbrNJROqyamprU1tZWOgZtxPGK9mL+/PkplUrZYost0qNHj0rHgXflfAoA7ZNCbBU1NDSkrq4uzc3NlY4Cq6S6ujqlUsmb+AJwvAJYc5xPAaB9UoitoqampjQ3N+fKK69MXV1dpeN0ON/85jczd+7cXHrppZWO0iGVSqVMmjQpTU1N3sAXgOMV7cmMGTPy+c9/PjfeeGOGDh1a6TjwjpxPAaD9Uoi9R3V1dRk3blylY3Q4AwYMyOLFi21bWI0cr2gP5s2blyT5wAc+kOHDh1c4DQAAHZWb6gMAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACqVLpQMUVUNDQ5qamiodY601b968LFiwIDNnzqx0lLVaTU1NamtrKx2DDs7xirY0e/bsJMljjz2WuXPnVjgNReKcCgDFohCrgIaGhtTV1aW5ubnSUdZ6W265ZaUjrNWqq6tTKpW8gWeNcbyiUvbaa69KR6BgnFMBoFgUYhXQ1NSU5ubmXHnllamrq6t0HNqpUqmUSZMmpampyZt31hjHK6AInFMBoHgUYhVUV1eXcePGVToGwLtyvAIAADoSN9UHAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQo01NmDAhxx13XKVjANAOPPvss6mqqsrDDz9c6SgAAHQwCjEAAAAACkUhRpuZPHly7rrrrlx44YWpqqpKVVVVampqcs4557Q8Z5999knXrl2zcOHCJMlf/vKXVFVV5emnn06SvPzyyzn00EPTv3//VFdXZ4899sjs2bMrsj4AvHe33nprtt9++/Tr1y8DBw7MXnvtlTlz5iRJNtpooyTJFltskaqqqkyYMKGCSQEA6EgUYrSZCy+8MNtss02OOOKINDY2prGxMYccckjq6+uTJOVyOXfffXf69euXe+65J0ly1113Zf3118+IESOSvF6qPfjgg5k6dWruu+++lMvl7LnnnlmyZEmlVguA9+CVV17JV77ylTz44IO5884706lTp+y7775Zvnx57r///iTJb3/72zQ2Nub666+vcFoAADqKLpUOQHGss8466datW6qrqzNkyJAkyU477ZQpU6Zk2bJleeyxx9KtW7cccMABqa+vz+677576+vqMHz8+STJ79uxMnTo106dPz7bbbpskueqqqzJs2LDccMMN2X///Su2bgCsmo9//OOtHv/0pz/NoEGD8vjjj2fQoEFJkoEDB7acNwAAYHVwhRgVtcMOO2TBggX54x//mLvuuivjx4/PhAkTWq4au+uuu1o+IlMqldKlS5d86EMfapl+4MCBGTVqVEqlUgXSA/BezZ49OwceeGA23njj9O3bNxtuuGGSpKGhobLBAADo0BRiVFS/fv0yduzY1NfXt5RfO+64Y/74xz/mqaeeyuzZs1uuEAOg4/nYxz6WefPm5bLLLsuMGTMyY8aMJMnixYsrnAwAgI5MIUab6tatW5YtW9Zq2Pjx4zNt2rT8/ve/z4QJEzJgwIDU1dXlzDPPzNChQzNy5MgkSV1dXZYuXdryx1KSzJ07N08++WQ222yzNl0PAN67N47h3/rWt7Lzzjunrq4uL7/8csv4bt26JcmbzhsAAPBeKcRoUxtuuGFmzJiRZ599Nk1NTVm+fHkmTJiQ2267LV26dMmmm26aJJkwYUKuuuqqVleHbbLJJtl7771zxBFH5J577smsWbMyadKkrL/++tl7770rtUoArKL+/ftn4MCB+fGPf5ynn346v/vd7/KVr3ylZfy6666bnj175tZbb80LL7yQv//97xVMCwBAR6IQo00df/zx6dy5czbbbLMMGjQoDQ0N2WGHHbJ8+fJW5deECROybNmylvuHvWHKlCnZcssts9dee2WbbbZJuVzOzTffnK5du7bxmgDwXnXq1CnXXHNNHnrooXzgAx/Il7/85Xz/+99vGd+lS5dcdNFFufTSS7Peeuv5nx8AAKw2vmWSNjVy5Mjcd999bxq+fPnyVo/32WeflMvlNz2vf//++Z//+Z81lg+AtrXLLrvk8ccfbzXs34//n/3sZ/PZz362rWMBANDBuUIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFEqXSgdg5TQ0NKSpqanSMda4Z555JkuWLMnIkSMrHaVN1NTUpLa2ttIxYLVr62PWU089la5du2ajjTZqs2Uma+413Nbbb+HChXnqqadSV1eXnj17ttly25uZM2dm2LBhGTRoUKWjrLWeeOKJ9OzZM8OHD2/T5Xak82kl3vP98Y9/zPrrr5911123TZfbnnS08wxAkSnE2pGGhobU1dWlubm50lFYzaqrq1MqlbzRoUMp0jFrTbyGi7T9YHXpKOdTr3/+U0fZtwHWJgqxdqSpqSnNzc258sorU1dXV+k4a9Q3v/nNzJ07N5deemmlo6xxpVIpkyZNSlNTkzc5dCiVOGYdeeSRGTx4cE4//fQ2WV6y5l7Dldh+jz76aCZPnpxrrrkmm2yySZsssz3acsst861vfSv77rtvpaOstT796U9nxIgROemkk9psmR3pfFqp93zbbrttvvjFL+bAAw9ss2W2N0ceeWQGDRqUM888s82W2ZH2bYC1iUKsHaqrq8u4ceMqHWONGjBgQBYvXtzh1xOKoC2PWb17986AAQM61LGjLbffkiVLkiSbbbZZRo8e3SbLbK+GDx/eofaz1a1Xr16pqamxjd6jtn7P16lTpwwbNszv7R306dOnw51nAIrKTfUBAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIXSpdIBaDsNDQ1pamqqdIwVMm/evCxYsCAzZ86sdJQVVlNTk9ra2krHgA5hVY9XCxcuzLx581b52NGRXsersg2feOKJJMnjjz+eJUuWrIlYHcZzzz3Xrs5Rbe2VV15JU1OT12KFrOoxdPny5Xn++eft2+9gwYIFzjMAHYRCrCAaGhpSV1eX5ubmSkdZKVtuuWWlI6yw6urqlEolb3LgPVodx6ubb755labrKK/j97oNP/WpT63mRB3PGWeckTPOOKPSMdZqjz32WG644YZVmrajvBYr4b2+/s8555ycc845qzlVx3Prrbeu0nT2bYC1h0KsIJqamtLc3Jwrr7wydXV1lY7T4ZRKpUyaNClNTU3e4MB7VKnjVUd6HTvm0551pNdiJXj9r73s2wBrF4VYwdTV1WXcuHGVjgF0cBMmTMh66633nubR3o5X06ZNW+vytrdtCKw+HfH1P3369Hzuc5/LE088kY9+9KOrfAUiACRuqg9AO1NfX5+qqqrMnz+/0lEAaENf+cpXsvnmm+eZZ57J5ZdfXuk4ALRzCjEAAGCtN2fOnOy0007ZYIMN0q9fv0rHAaCdU4gBsEYsX748STJx4sQMGTIkp556asu48847L6NHj06vXr0ybNiwfP7zn8/ChQtbTX/cccelf//+6dWrV97//vfn5ptvzrPPPpuJEycmSfr375+qqqpMnjy5ZXlnn312Ntpoo/Ts2TNjx47Ndddd1zK/N64su/POO/PBD34w1dXV2XbbbfPkk0+2Wm59fX3GjRuXHj16ZOONN85pp52WpUuXtoyfPXt2dtxxx/To0SObbbZZ/vCHP6zOzUYbWrRoUb70pS9l3XXXTY8ePbL99tvngQceSLLi+8tvfvObbLXVVunRo0dqamqy7777tpr/8ccfn/XXXz+9evXKhz70odTX17flKkK78navyWeffTZVVVWZO3duDjvssFRVVblCDID3TCEGwBpx4403JkmuuOKKfO9738u3v/3t3HHHHUmSTp065aKLLsqf/vSnXHHFFfnd736Xr33ta62mX7x4cX7/+9/n0UcfzXe/+9307t07w4YNyy9/+cskyZNPPpnGxsZceOGFSZKzzz47//M//5Mf/ehH+dOf/pQvf/nLmTRpUu66665W8z3xxBNz7rnn5sEHH0yXLl1y2GGHtRp/8skn59hjj83jjz+eSy+9NJdffnnOPPPMJK+Xbvvtt1+6deuWGTNm5Ec/+lEuuuii1b/xaBNf+9rX8stf/jJXXHFFZs6cmREjRmS33XbLvHnzWp7zTvvLTTfdlH333Td77rln/vjHP+bOO+/M1ltv3TL+mGOOyX333ZdrrrkmjzzySPbff//svvvumT17dpuuJ7QXb/ea7NOnTxobG9O3b99ccMEFaWxszAEHHFDpuAC0c26qD8Aasckmm+Thhx9ObW1txo0blx/+8Ie5884785GPfCTHHXdcy/M23HDDnHHGGfnc5z6Xiy++uGX45ptvntGjRydJNt5445bhAwYMSJKsu+66LR+ZWbRoUc4666z89re/zTbbbNMyzT333JNLL70048ePb5n+zDPPbHn89a9/PR/96Efz2muvtYyfPHlyPv3pT7fM4/TTT8/Xvva1nHLKKfntb3+bJ554IrfddlvLlwYcc8wx+eIXv7i6Nhtt5JVXXskll1ySyy+/PHvssUeS5LLLLssdd9yR//f//l+22mqrJG+/v/To0SNnnnlmPvWpT+W0005rme/YsWOTJA0NDZkyZUoaGhpa9pXjjz8+t956a6ZMmZKzzjqrLVcX1nrv9Jr86U9/mv/+7/9OVVVV1llnnQwZMqTCaQHoCBRiAKwRbxRibxg6dGhefPHFJMlvf/vbnH322XniiSfyj3/8I0uXLs1rr72W5ubmluf/5Cc/yaOPPppddtklH//4xzNmzJi3XdbTTz+d5ubmfOQjH2k1fPHixdliiy1aDfv3+QwdOjRJWnK9sdx//yjOsmXLWrKVSqUMGzas1TdovlHa0b7MmTMnS5YsyXbbbdcyrGvXrtl6661TKpVaCrG3219qa2vz8MMP54gjjnjL+T/66KNZtmxZRo4c2Wr4okWLMnDgwNW9OtDuvdtrEgBWN4UYAGtEly6tTzFVVVVZvnx5nn322ey11145+uijc+aZZ2bAgAG55557cvjhh2fx4sUtz586dWoaGhpy++235+yzz8655577tldivXH/sZtuuinrr79+q3Hdu3dv9bhr166tMiX/ut9Zkhx11FFvuZwePXqsyGrTwbzT/tKzZ8+3nW7hwoXp3LlzHnrooXTu3LnVuN69e6+BpAAArAz3EAOgTT300ENZvnx5zj333Hz4wx/OyJEj87e//e1NzxsyZEg+97nP5frrr89Xv/rVXHbZZUmSbt26JXn9yq03bLbZZunevXsaGhoyYsSIVv+GDRu2Uvmee+65N81jxIgR6dSpU+rq6vL888+nsbGx5fmPPvroqmwGKux973tfunXrlunTp7cMW7JkSR544IFsttlmKzSPMWPG5M4773zLcVtssUWWLVuWF1988U37ko97wZutjtckAKwMV4gB0KZGjBiRJUuW5Ac/+EE+9rGPZfr06fnRj370pufde++96d+/f15++eVMmzYtdXV1SZLhw4enqqoqN954Y/bcc8/07Nkzffr0yfHHH58vf/nLWb58ebbffvv8/e9/z/Tp09O3b9+We4KtiBtvvDGnnXZaPvGJT6RTp06ZNWtWHnvssZxxxhnZZZddMnLkyHz605/O97///fzjH/9odd8z2o9evXrl6KOPzn//939nwIABqa2tzfe+9700Nzfn8MMPz6xZs951Hqecckp23nnnvO9978unPvWpLF26NDfffHNOOOGEjBw5MgcffHAOPfTQnHvuudliiy3y0ksv5c4778yYMWPy0Y9+tA3WEtqPd3tNAsDq5gox2p0lS5ZUOgLwHowdOzbnnXdevvvd7+YDH/hArrrqqpx99tlvet53v/vd1NXVZffdd8/IkSNbiqf1118/p512Wr7+9a9n8ODBOeaYY5Ikp59+ek466aScffbZLdPddNNN2WijjVYq3wUXXJDbb789W221VT784Q/n/PPPz/Dhw5O8/u2Yv/rVr/Lqq69m6623zmc/+9l8/vOff49bhEr5zne+k49//OM55JBDMm7cuDz99NO57bbb0r9//xWafsKECfnf//3fTJ06NZtvvnl22mmn3H///S3jp0yZkkMPPTRf/epXM2rUqOyzzz554IEHUltbu6ZWCdq19/qaBICV4Qox1qgf//jHOfXUU/OXv/wlnTr9q3/de++9M3DgwPz0pz/Nr3/965x22ml5/PHHs9566+XTn/50TjzxxJb7D1VVVeXiiy/OLbfckjvvvDPHH398rrrqqnzuc5/L8ccf3zLPhx9+OFtssUVmz56dESNGtPm6Av9SX1+fmTNn5uqrr24ZdsMNN7T8/OUvfzlf/vKXW01zyCGHtHr861//OuPGjXvL+Z900kk56aSTWg2rqqrKsccem2OPPfYtp5kwYULK5XKrYZtvvnnLsHnz5iVJtt1225aS7a2MHDkyd999d8vjmTNnJkkmTpz4ttOwdurRo0cuuuiiXHTRRW8a9277yxv222+/7Lfffm85/65du+a0005r9S2UwNt7p9dkksyfP79tAwHQoblCjDVq//33z9y5czNt2rSWYfPmzcutt96agw8+OHfffXcOPfTQHHvssXn88cdz6aWX5vLLL8+ZZ57Zaj6nnnpq9t133zz66KM5/PDDc9hhh2XKlCmtnjNlypTsuOOOyjAAAADgHblCjDWqf//+2WOPPfLzn/88O++8c5LkuuuuS01NTSZOnJhdd901X//611vu77Pxxhvn9NNPz9e+9rWccsopLfM56KCD8pnPfKbl8eTJk3PyySfn/vvvz9Zbb50lS5bk5z//ec4555y2XUFgjSmVSis9zX333ZcePXpkiy22aJPlre1Wdp1efvnl3Hvvvdlhhx3St2/fNZSq/bvpppsyevRoH318B9OnT0+fPn0yZsyYlZ62I74WK2FVtuMtt9ySTTfddKU/al4kzjMAHYdCjDXu4IMPzhFHHJGLL7443bt3z1VXXZVPfepTLTernj59eqsrwpYtW5bXXnstzc3Nqa6uTpJ88IMfbDXP9dZbLx/96Efz05/+NFtvvXV+85vfZNGiRdl///3bdN2A1a+mpibV1dWZNGlSmy+7uro6NTU1bb7c1a2S2xBWh47yWqwEr/+1m30bYO2hEGON+9jHPpZyuZybbropW221Ve6+++6cf/75SZKFCxfmtNNOe8v7r/To0aPl5169er1p/Gc/+9kccsghOf/88zNlypQccMABLQUa0H7V1tamVCqlqalppac98sgjM3jw4Jx++umrtOyampoOcdXPqm7DRx99NJMnT84111yTTTbZZA2la/+23HLLfOtb38q+++5b6ShrrU9/+tMZMWLEm+71t6I6ymuxEt7LMXTbbbfNF7/4xRx44IFrIFnHcOSRR2bQoEFvur3HirJvA6w9FGKscT169Mh+++2Xq666Kk8//XRGjRrVcqPscePG5cknn1yl+37tueee6dWrVy655JLceuut+f3vf7+6owMVUltbu0p/MPTu3TsDBgx425vxF8mqbMM3vsV3s802y+jRo9dErA5j+PDh9rN30KtXr9TU1NhGFbKqx9BOnTpl2LBhfm/voE+fPs4zAB2EQow2cfDBB2evvfbKn/70p1aX8J988snZa6+9Ultbm0984hMtH6N87LHHcsYZZ7zjPDt37pzJkyfnG9/4RjbZZJNss802a3o1AAAAgA7At0zSJnbaaacMGDAgTz75ZA466KCW4bvttltuvPHG3H777dlqq63y4Q9/OOeff36GDx++QvM9/PDDs3jx4lY33AcAAAB4J64Qo0106tQpf/vb395y3G677Zbddtvtbactl8tvO+6vf/1runbtmkMPPfQ9ZwQAAACKQSFGu7Ro0aK89NJLOfXUU7P//vtn8ODBlY4EAAAAtBM+Mkm7dPXVV2f48OGZP39+vve971U6DgAAANCOKMRolyZPnpxly5bloYceyvrrr1/pOAAAAEA7ohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAAqlS6UD0D40NDSkqampzZb3/PPPZ+nSpdloo43abJlJUlNTk9ra2jZdZkfT1vtKY2NjFi5cmE022aTNlpnYVwAAANozhRjvqqGhIXV1dWlubq50lDWuuro6pVJJ0bGK7CsAAAC0Bwox3lVTU1Oam5tz5ZVXpq6urk2W+c1vfjNz587NpZde2ibLS5JSqZRJkyalqalJybGKKrGvXHjhhamvr8+vfvWrNlleYl8BAABo7xRirLC6urqMGzeuTZY1YMCALF68uM2Wx+rVlvvK4MGD0717d/sKAAAAK8xN9QEAAAAoFIUYAAAAAIWiEAMAAACgUNxDDIA1qlQqtdmyFi5cmHnz5mXmzJlttsw1vX5tuf2eeOKJJMnjjz+eJUuWtNly26PnnnuuTfez9uaVV15JU1NTh3otVkJbr9Py5cvz/PPP27ffwYIFCzrceQagqBRiAKwRNTU1qa6uzqRJk9p82TfffHObLq+6ujo1NTWrdZ6V3H6f+tSn2nyZ7c0ZZ5yRM844o9Ix1mqPPfZYbrjhhjZd5pp4LVZCJV//55xzTs4555w2X257c+utt7bp8jrKvg2wNlGIAbBG1NbWplQqpampqdJR1riamprU1tau1nkWafvB6rImXouV4PXPf+oo+zbA2kQhBsAaU1tb6w38e2D7QXF5/QPAmuWm+gAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKpUulA9CxNTQ0pKmpaaWnGzNmTDbZZJPMnDlzlZZbU1OT2traVZqWylmV/aW2tjYf+9jH7CsAAACsMIUYa0xDQ0Pq6urS3Ny8yvP49re/vUrTVVdXp1QqKTrakfe6v5x33nmrNJ19BQAAoHgUYqwxTU1NaW5uzpVXXpm6urqVnv5vf/tbPvaxj+XnP/95Ro0alSR5+OGHc9ZZZ+XZZ5/N9ttvn/POO+9Nw4444ohMmjQpTU1NSo525L3uL6uiVCq9aV859dRTc8MNN+Thhx9ukwwAAAC0PYUYa1xdXV3GjRv3js+ZPHly5s+fnxtuuKFl2NixY9PY2Jiampp06fL6rnr00Udnm222SX19fXr37p1+/fq9adif//znNbk6rGErsr/8JyUWAAAAK8NN9Vlrde7cOUOGDGkpw5Jkzpw52WmnnbLBBhukX79+bzuMYiiXy1m6dGmlYwAAANDOKMRoU9ddd11Gjx6dnj17ZuDAgdlll13y3//937niiivy61//OlVVVamqqkp9fX2effbZVFVV5eGHH275ee7cuTnssMNSVVWVyy+//C2HsfaaMGFCjjnmmBxzzDFZZ511UlNTk5NOOinlcrnlOZMmTUqfPn0yZMiQHHTQQXnxxRdbxtXX16eqqiq33HJLttxyy3Tv3j1XXnllTjvttMyaNatl/3ljP5g/f34++9nPZtCgQenbt2922mmnzJo16025PvKRj6RPnz45/PDD89prr63x7QAAAEBlKcRoM42NjTnwwANz2GGHpVQqpb6+Pvvtt19OOeWUfPKTn8zuu++exsbGNDY2Ztttt2017bBhw9LY2Ji+ffvmggsuSGNjY/bff/83DTvggAMqtHasqCuuuCJdunTJ/fffnwsvvDDnnXdefvKTn7SMP/roozNr1qzccMMNefbZZzN58uQ3zePrX/96vvOd76RUKuUjH/lIvvrVr+b9739/y/7zxn6w//7758UXX8wtt9yShx56KOPGjcvOO++cefPmJUluv/32JMkXvvCFPPjggxk6dGguvvjiNb8RAAAAqCj3EKPNNDY2ZunSpdlvv/0yfPjwJMno0aOTJD179syiRYsyZMiQt5z2jY9PVlVVZZ111ml5Xq9evd40jLXbsGHDcv7556eqqiqjRo3Ko48+mvPPPz9XXnllkmS77bbLxhtvnI033jgXXXRRttpqqyxcuDC9e/dumce3v/3tfOQjH2l53Lt373Tp0qXVPnDPPffk/vvvz4svvpju3bsnSc4555zccMMNue6663LkkUfm6quvTpLss88+GTVqVM4444z89re/dZUYAABAB+cKMdrM2LFjs/POO2f06NHZf//9c9lll+Xll1+udCza2Ic//OFUVVW1PN5mm20ye/bsLFu2LEly3HHHpba2Nn369Mn48eOTJA0NDa3m8cEPfvBdlzNr1qwsXLgwAwcOTO/evVv+PfPMM5kzZ06S5JlnnnnTdNtss80qrxsAAADtgyvEaDOdO3fOHXfckXvvvTe33357fvCDH+TEE0/MjBkzKh2NtcDixYuTvH7V31VXXZVBgwaloaEhu+22W8u4N/Tq1etd57dw4cIMHTo09fX1bxrnyxcAAACKTSFGm6qqqsp2222X7bbbLieffHKGDx+eX/3qV+nWrVvLFUJ0bP9ZgP7hD3/IJptskmeffTZJ8sUvfjE77LBDkuTBBx9coXm+1f4zbty4/N///V+6dOmSDTfc8C2n22ijjfLII4+8KQ8AAAAdm49M0mZmzJiRs846Kw8++GAaGhpy/fXX56WXXkpdXV023HDDPPLII3nyySfT1NSUJUuWVDoua0hDQ0O+8pWv5Mknn8zVV1+dH/zgBzn22GNb7v917bXX5s9//nOmTp2a008/fYXmueGGG+aZZ57Jww8/nKampixatCi77LJLttlmm+yzzz65/fbb8+yzz+bee+/NiSee2FK0HXjggUmSqVOn5qmnnsopp5ySP/3pT2tmxQEAAFhrKMRoM3379s3vf//77Lnnnhk5cmS+9a1v5dxzz80ee+yRI444IqNGjcoHP/jBDBo0KNOnT690XNaQQw89NK+++mq23nrrfOELX8ixxx6bI488Mv3790+S/Pa3v81mm22W73znOznnnHNWaJ4f//jHs/vuu2fixIkZNGhQrr766lRVVeXmm2/OjjvumM985jMZOXJkPvWpT+W5557L4MGDkyS77rprkuTCCy/Mlltumeeeey5HH330mllxAAAA1ho+Mkmbqaury6233vqW4wYNGpTbb7/9TcPL5XKrx/Pnz3/Tc95qGGuvrl275oILLsgll1zyluN/85vfZNy4cS2P/30fmDBhwpv2iSTp3r17rrvuujcN79OnTy666KJcdNFF75jpzjvvbLXM7373u++6HgAAALRfrhADAAAAoFBcIcYaVyqVVnqaRx99NIsWLcoHP/jBNlkea4+V/f3Nnj07L774Yrbbbrs1vqwis61oK3Pnzs3MmTOz/fbbp2fPnpWOQ0E4xgFA8SjEWGNqampSXV2dSZMmtfmyq6urU1NT0+bL5Z3V19e/7bhK7S/2lXdWydcxQFtyPgCAYlGIscbU1tamVCqlqalppaf95je/mblz5+bSSy9dpWXX1NSktrZ2laalMlZ1f7nwwgtTX1+fX/3qV6u0XPvKO3svr2NYFTNmzMjnP//53HjjjRk6dGil41AgzgcAUCwKMdao2traVXpzOWDAgCxevLjVjc7p+FZlfxk8eHC6d+9uX1mDVvV1DKti3rx5SZIPfOADGT58eIXTAADQUbmpPgAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgULpUOgDtR6lUarNlzZs3LwsWLMjMmTPbbJltuX4dXVtuyxdeeCGLFi2yr6yA9pqbYpk9e3aS5LHHHsvcuXMrnAbemeMqALRfCjHeVU1NTaqrqzNp0qQ2X/aWW27Zpsurrq5OTU1Nmy6zI7GvrJ0q+XuBVbXXXntVOgKskPZ0PgAA/kUhxruqra1NqVRKU1NTpaOscTU1Namtra10jHbLvrJ2KtLvBaCttafzAQDwLwoxVkhtba03e6wQ+8raye8FAADgX9xUHwAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKpUulA9C2SqVSpSOskFKplMWLF2fs2LGVjrJC2st2pWOwv1EkLz/5bJY0v5Z1t9i00lHWWo2vvZKnFs7P+Jr1Kx1l7dW8KN0eeiZLRg9LuV+vSqeBNuM9A8DbU4gVRE1NTaqrqzNp0qRKR+mwqqurU1NTU+kYdGBexxTRrhmcmnTPz9NQ6Shrr63HpOqTu6d8/PcqnWStVZvq3NN9pxy4+L5ML8+tdBxoU96jArw1hVhB1NbWplQqpampqdJRVsg3v/nNzJ07N5deemmlo6ywmpqa1NbWVjoGHVh7ex3D6nD/GZdl/tPP56HLf1XpKGutG/42J2c8+WAeeuihSkdZa1X949Vk9+/k0rPOzaJdR1c6DrQp71EB3ppCrEBqa2vbzclwwIABWbx4ccaNG1fpKLBWaU+vY1gd5gysyZLGec4H72Bml6XJkw/aRu/ibxsNyXovLUp/2wkAiJvqAwBQAN23f38W3fOnSscAANYSCjEAADq87tt/IEseeSbLX15Y6SgAwFpAIQYAQIfXY5ctki6ds/Ant1Q6CgCwFlCIAQDQ4XVZf2B6H7ZrFpx3fZb//ZVKxwEAKkwhBgBAIfQ94ZMpv7o4Cy76daWjAAAVphADAKAQOq83ML2P/mgWnPvLLHrwqUrHAQAqSCEGAEBhrHPKpHQds1Ga9j8zS/86t9JxAIAKUYgBAFAYVT26peYXJ6aqc6c0feL0LG9+rdKRAIAKUIgBAFAonYf0T831J2fpU3/NS3uenGUvvFzpSABAG1OIAQBQON3GbJRBt5yRpc/+X17Y7itZPOvPlY4EALQhhRgAAIXUfetRGXzPeek0aJ28OPFraf7lPZWOBAC0EYUYAACF1WWDmqx753fSY8+tMvfg76bpk2dm6XMvVjoWALCGKcQAACi0TtU9MvBnX8vAn30tix98Kv+3+dH5x/f/N+XFSyodDQBYQxRiAAAUXlVVVar33yFDZl2SXkfumb+femX+74NfzCtX16e8ZGml4wEAq5lCDAAA/qlTn+r0/+7hGTzjwnTZaEjmfebcNG52ZBb8cGqWL3y10vEAgNVEIQYAAP+h2wc2zKBfn5rBD1yU7jt8IPO//tM0bnJY5p/8P1n67AuVjgcAvEcKMQAAeBvdRm+UgT/9SoY+/uNUH7JzFl58Yxo3/WxemPi1LPzxzVnW9PdKRwQAVoFCDAAA3kWX2nXT/3ufzXrPXpEBP/1KOvWtzstfvjR/2/DTeWnf0/LKtXdl+csLKx0TAFhBXSodgJVXKpUqHWGNmzdvXhYsWJCZM2dWOsoaV4TfJwB0FJ1690yvgyam10ETs+zF+Wn+5T1pvqY+8z59TtKpU7pusXF6TBibHhPGpNu2m6VTrx6VjgwAvAWFWDtSU1OT6urqTJo0qdJR2syWW25Z6Qhtorq6OjU1NZWOAQCshM7r9kufo/dKn6P3ytLnXsxrv3s4i+ofyStX/S4Lzv1l0rVLum89Kt0n/rMg23pkqrp1rXRsACAKsXaltrY2pVIpTU1NlY7CalZTU5Pa2tpKxwAAVlGX4eum92d2Te/P7JpyuZylTzyf1+ofyaL6R7Lgh7/OP874eaqqu6f7du9P9+3fn64f2DBdNx2Wzhuum6rOnSsdHwAKRyHWztTW1ipOAADWYlVVVelaV5uudbXpc/ReKS9bliWznvlnQTYr/zj3lyn/o/n15/bsli6jNkjXTWvTdbPadK0bli51temy0WBFGQCsQQoxAABYg6o6d063cSPSbdyI5Cv7pVwuZ9nf5mVpqSFLSg1ZUno+Sx5vyKu3PJDy3195faLuXdN11AbpuumwdN2sNl3qXi/Mumw0JFVdFGUA8F4pxAAAoA1VVVWly/oD02X9gemxyxYtw8vlcpY3znu9IHuiIUsef70se+2Omf/6BstuXdJ15AbpMmr9dB46MJ0H90vnwf3T6Z//7bxuv3Rat5/SDADehUIMAADWAlVVVem83sB0Xm9geuy8ecvwcrmc5S/M/+fVZK+XZEuf+muW/Kkhy194+V9l2b9mlE4D+7xelK37z6JsSL90Wrf/6wXauv3Sacg/y7Oavj6aCUAhKcQAAGAtVlVVlc5D+qfzkP7pMXHsm8aXFy3JshfnZ/mL87PshZez7IX5Wf7P/y578eUse/6lLH5odpa98HLLvctadOqUToP6vl6e9e+Tqupuqarunqqe3VNV3T2d/u3nql493nr4G//+/XGPbqmqqmqjLQQAK08hBgAA7VhV967pMmxQMmzQuz53+auLsvyF+a8XaG+UZi+8/HpZNv+VlF9dlOXzX0n5b/NSfnVRys3//PfGz68uXsFQVW8uyrp1STp3SjpVparT6/9Np06tfk7nTqnqVPUWjzv9a9qqf/2cTlWvX+H2xjSdX59fVc9urQu6tyv4/mNcundV5AEUhEIMAAAKolPP7um04eB02XDwKk1fXr485dcWp/zKfxRlzYuy/N8LtLcYl0VLUl6+PFleTpa9/t/y8uUtP2f58n+N/+fw8rLlKS9dnixb8s/n/2tcy/Tl1+f37/Muv7a4dZbX3kORV909nf7jarhONX3T+Z8fR33j46edB/dPp0HruH8bQDuhEAMAAFZIVadOqarukVT3qHSUlVJevjzlVxe/bVnXMvyV195+3D9/Xt70jyz+45y3v3/bP8uyTv/8koPOQ/7t53/7AoROA/u4fxtABSnEAACADq2qU6dU9eqR9Fq9Rd4b929b9sLLr9/D7f/++d83Por6/EtZ/OBTWfbi/Le+f9u667xelK0/MF03HZaudbXpUlebrptukE59qldrVgBaU4gBAACsgjfu39ZlZe7f9m/l2Rtl2rKGl9L8q3uz7PxftTy/87BB6Vo3LF03q02XTWvTdbPadN10WDr1VZQBrA4KMQAAgDVsRe7ftvyV17L0yb9kSanhn/+eT/Ov/5BlF/769XulJem8fs3rJVnd61eUvf5vWDqt06utVgWgQ1CIAQAArAU69eqRbuNGpNu4Ea2GL29+LUuf+muWPP56Sbb0iYa8dtP9WfiDqf8qyjaoSfcdPpDuE8amx4Qx6TJ83UqsAkC7oRADAABYi3Wq7pFum78v3TZ/X6vhy19d9HpRVmrIkll/zmt3PZrma+5KyuV02Xhouk8Ykx4TxqT7hDHpvG6/yoQHWEspxAAAANqhTj27p9vYjdNt7MbJpyYkSZbNW5BFdz+WRdNm5bX6R/LKT29LknR9//B/FWTjx7gXGVB4CjEAAIAOovOAPqnee5tU771NkmRZ47y8Vv9IFtU/kld/MyML/7/fJN27pudHt071p8an524fTFX3rhVODdD2FGIAAAAdVOehA9LrwAnpdeCEJMnSP/9fmm+4N83X1GfuJ89KVb9eqd5v+1R/any6b//+VHXqVNnAAG1EIQYAAFAQXTYekr5f2S99v7Jfljz+XF655q40X3tXXvnpbemy8dD0OW6fVB+yczr17F7pqABrlPofAACggLpuNjz9vn1ohj7xk6x753fSddz78vJxl6Zx5OH5+5lXZ9ncf1Q6IsAaoxADAAAosKqqqnTf7v2pufKEDH3s0lR/fPssOOe6NG5yWF7+2k+yfP7CSkcEWO0UYgAAACR5/SOV/S/4XIbO/mn6fHm/vPLT29M49ui88vNpKZfLlY4HsNooxAAAAGilc806WeekgzJk1iXpvsMHMu+w8/LSrt/Mksefq3Q0gNVCIQYAAMBb6rL+wNRceUIG3fjtLGucl//b+tgsvOyWSscCeM8UYgAAALyjHrtskSEP/TC9P7t7Xv7ixXn5y5emvHRZpWMBrLIulQ4AAADA2q+qe9f0v+Bz6fr+4Xn5y5dmyRPPZ+BVJ6TzgD6Vjgaw0lwhBgAAwArrfcQeGXTT6Vky689p+q9TsvzVRZWOBLDSFGIAAACslB7jR2fQjd/Okj89l5ePvMg3UALtjkIMAACAldZt3IgM+H9fSfP//j7/OPvaSscBWCkKMQAAAFZJ9X7bpe8pk/KPb1+VRfeVKh0HYIW5qX4FlUpOGG9n9uzZWbJkSTbbbLNKR1lr2X8AAFgb9D1h/7x6/T35+yk/y6DbzkxVVVWlIwG8K4VYBdTU1KS6ujqTJk2qdBTauerq6tTU1FQ6BgAABVbVqVPWOWVSmj5xRhZNeyQ9dhpb6UgA70ohVgG1tbUplUppamqqdJS11je/+c3MnTs3l156aaWjrNVqampSW1tb6RgAABRcj49unW5bjczfT79KIQa0CwqxCqmtrVVkvIMBAwZk8eLFGTduXKWjAAAA76Kqqiq9j9gj8466KMvmLUjnAX0qHQngHbmpPgAAAO9Z9x0+kJTLWezm+kA7oBADAADgPeu84eB0Xr8mi+55rNJRAN6VQgwAAID3rKqqKt0+PCqLH3q60lEA3pV7iAEAAPAmDQ0NK/1FYL3Li9P1by9m5syZaygV8IbFi5ek8f8aM3Pm8pWe1he0KcQAAAD4Dw0NDamrq0tzc/NKTbdpVZ+sk66ZseX/W0PJgBbrvj+XXTcvly1sXOlJq6urUyqVCl2KKcQAAABopampKc3NzbnyyitTV1dX6TjAalQqlTJp0qQ0NTUpxAAAAOA/1dXVZdy4cZWOAbDauak+AAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAAWy4YYb5oILLqh0DKgohRgAAAC0kcmTJ6eqqipVVVXp1q1bRowYkW9/+9tZunRpm2V44IEHcuSRR67WeV5++eXp16/fap0nrEldKh0AAAAAimT33XfPlClTsmjRotx88835whe+kK5du+Yb3/hGmyx/0KBBbbIcWJu5QgwAAADaUPfu3TNkyJAMHz48Rx99dHbZZZdMnTo1ixYtyvHHH5/1118/vXr1yoc+9KHU19e3TPfGVVi33XZb6urq0rt37+y+++5pbGxsec7kyZOzzz775JxzzsnQoUMzcODAfOELX8iSJUtanvOfH5msqqrKT37yk+y7776prq7OJptskqlTp7bKPHXq1GyyySbp0aNHJk6cmCuuuCJVVVWZP39+6uvr85nPfCZ///vfW65+O/XUU5MkL7/8cg499ND0798/1dXV2WOPPTJ79uyVWidYExRiAAAAUEE9e/bM4sWLc8wxx+S+++7LNddck0ceeST7779/dt9991YFUnNzc84555z87Gc/y+9///s0NDTk+OOPbzW/adOmZc6cOZk2bVquuOKKXH755bn88svfMcNpp52WT37yk3nkkUey55575uCDD868efOSJM8880w+8YlPZJ999smsWbNy1FFH5cQTT2yZdtttt80FF1yQvn37prGxMY2NjS2ZJk+enAcffDBTp07Nfffdl3K5nD333LNVQbci6wSrm49MvkelUqnSETqkefPmZcGCBZk5c2alo3RI9lsAAKi8crmcO++8M7fddlsOPPDATJkyJQ0NDVlvvfWSJMcff3xuvfXWTJkyJWeddVaSZMmSJfnRj36U973vfUmSY445Jt/+9rdbzbd///754Q9/mM6dO2fTTTfNRz/60dx555054ogj3jbL5MmTc+CBByZJzjrrrFx00UW5//77s/vuu+fSSy/NqFGj8v3vfz9JMmrUqDz22GM588wzkyTdunXLOuusk6qqqgwZMqRlnrNnz87UqVMzffr0bLvttkmSq666KsOGDcsNN9yQ/ffff4XXCVY3hdgqqqmpSXV1dSZNmlTpKB3alltuWekIHVZ1dXVqamoqHQMAAArnxhtvTO/evbNkyZIsX748Bx10UD7xiU/k8ssvz8iRI1s9d9GiRRk4cGDL4+rq6pbiKEmGDh2aF198sdU073//+9O5c+dWz3n00UffMdOYMWNafu7Vq1f69u3bMt8nn3wyW221Vavnb7311u+6nqVSKV26dMmHPvShlmEDBw7MqFGjWv1P+hVZJ1jdFGKrqLa2NqVSKU1NTZWOAqukpqYmtbW1lY4BAACFM3HixFxyySXp1q1b1ltvvXTp0iXXXnttOnfunIceeqhVmZUkvXv3bvm5a9eurcZVVVWlXC63GvZWz1m+fPk7ZlqVaVaXFVknWN0UYu9BbW2tQgEAAICV0qtXr4wYMaLVsC222CLLli3Liy++mB122KFCyd7aqFGjcvPNN7ca9sADD7R63K1btyxbtqzVsLq6uixdujQzZsxo+cjk3Llz8+STT2azzTZbs6HhXbipPgAAAFTYyJEjc/DBB+fQQw/N9ddfn2eeeSb3339/zj777Nx0000VzXbUUUfliSeeyAknnJCnnnoqv/jFL1pu0l9VVZXk9W+uXLhwYe688840NTWlubk5m2yySfbee+8cccQRueeeezJr1qxMmjQp66+/fvbee+8KrhEoxAAAAGCtMGXKlBx66KH56le/mlGjRmWfffbJAw88UPFPJm200Ua57rrrcv3112fMmDG55JJLWr5lsnv37kle/6bJz33ucznggAMyaNCgfO9730vy+jptueWW2WuvvbLNNtukXC7n5ptvftPHJKGtVZUL/sHcmTNnZsstt8xDDz2UcePGVToO0ME55sDK+d8jTsr/PfZUvnjftZWOstb6ySP358g7fpnlX/1upaMAHYj3LLybM888Mz/60Y/y/PPPVzoKK8nr+3XuIQYAAAC8o4svvjhbbbVVBg4cmOnTp+f73/9+jjnmmErHglWmEAMAAADe0ezZs3PGGWdk3rx5qa2tzVe/+tV84xvfqHQsWGUKMQAAAOAdnX/++Tn//PMrHQNWGzfVBwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKF0qXQAAAAASJKGhoY0NTVVOsYa9be//S1/+MMf8tGPfjTdu3evdJy10quvvppbbrkl22yzTYYOHVrpOGtcTU1NamtrKx2jcBRiAAAAVFxDQ0Pq6urS3Nxc6Sht4swzz6x0BNYS1dXVKZVKSrE2phADAACg4pqamtLc3Jwrr7wydXV1lY6zxkybNi3HH3987rzzzvTr16/ScdZKL730UnbfffdceOGF2X777SsdZ40qlUqZNGlSmpqaFGJtTCEGAADAWqOuri7jxo2rdIw1pqGhIUkyduzYDBw4sMJp1k6NjY1JkhEjRnTofYHKclN9AAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAolC6VDrC2KJVKlY7AO3j65ZczqLo663TvXukoa62//KMhXTt1y+DeQyodhXfgWAMAAFB5hS/EampqUl1dnUmTJlU6Cu/gw2ednT/fcENevH9GpaOstcZ8dpO82rQos29oqHQU3kV1dXVqamoqHQMAAKCwCl+I1dbWplQqpampqdJReAdfvOO3OfHEb2b7DTaodJS11gX3nZ11ew3JQSd9ptJReBc1NTWpra2tdAwAAIDCKnwhlrxeivnjdO1W9ds7U1tbm3Gbb17pKGutXo/0zsCBAzNu3LhKRwEAAIC1mpvqAwAAAFAoCjEAAAAACkUhBgAAAEChuIcYAAAA7VpDQ0O7+aK0OXPmJElmzZqVfv36VTbMWuqll15Kkjz99NOZOXNmhdOsOF+e1b4oxAAAAGi3GhoaUldXl+bm5kpHWSk777xzpSOs9Y499thKR1gp1dXVKZVKSrF2QiEGAABAu9XU1JTm5uZceeWVqaurq3QcCqpUKmXSpElpampSiLUTCjEAAADavbq6uowbN67SMYB2wk31AQAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAACD19fWpqqrK/PnzkySXX355+vXr1zL+1FNPzeabb16RbLC6KcQAAACgg/nRj36UPn36ZOnSpS3DFi5cmK5du2bChAmtnvtGETZ06NA0NjZmnXXWaeO00PYUYgAAANDBTJw4MQsXLsyDDz7YMuzuu+/OkCFDMmPGjLz22mstw6dNm5ba2tqMGjUqQ4YMSVVVVSUiQ5tSiAEAAEAHM2rUqAwdOjT19fUtw+rr67P33ntno402yh/+8IdWwydOnPimj0xCR6YQAwAAgA5o4sSJmTZtWsvjadOmZcKECRk/fnzL8FdffTUzZszIxIkTKxUTKkIhBgAAAB3QxIkTM3369CxdujQLFizIH//4x4wfPz477rhjy5Vj9913XxYtWqQQo3AUYgAAANABTZgwIa+88koeeOCB3H333Rk5cmQGDRqU8ePHt9xHrL6+PhtvvHFqa2srHRfaVJdKBwAAAABWvxEjRmSDDTbItGnT8vLLL2f8+PFJkvXWWy/Dhg3Lvffem2nTpmWnnXaqcFJoe64QAwAAgA7qjZvl19fXZ8KECS3Dd9xxx9xyyy25//77fVySQlKIAQAAQAc1ceLE3HPPPXn44YdbrhBLkvHjx+fSSy/N4sWLFWIUkkIMAAAAOqiJEyfm1VdfzYgRIzJ48OCW4ePHj8+CBQsyatSoDB06tIIJoTIUYgAAANBBbbjhhimXyymVSq2GDx8+POVyOU888UTLsAkTJqRcLqdfv35JksmTJ2f+/Pkt40899dQ8/PDDbZAa1jyFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhdKl0gEAAACgUhoaGtLU1NRmy2tsbMzMmTPzkY98JN26dWuz5bYnr776an73u99lq622yrrrrtumy66pqUltbW2bLpPKUIgBAABQSA0NDamrq0tzc3ObL/vkk09u82Xy7qqrq1MqlZRiBaAQAwAAoJCamprS3NycK6+8MnV1dW2yzGnTpuX444/PnXfemX79+rXJMtubl156KbvvvnsuvPDCbL/99m223FKplEmTJqWpqUkhVgAKMQAAAAqtrq4u48aNa5NlNTQ0JEnGjh2bgQMHtsky25vGxsYkyYgRI9rs90LxuKk+AAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQulQ6AAAAALRHDQ0NaWpqWqlp5syZkySZNWtW+vXrtwZStX8vvfRSkuTpp5/OzJkzV3r6mpqa1NbWru5YdDAKMQAAAFhJDQ0NqaurS3Nz8ypNv/POO6/mRB3Pscceu0rTVVdXp1QqKcV4RwoxAAAAWElNTU1pbm7OlVdembq6ukrH4Z9KpVImTZqUpqYmhRjvSCEGAAAAq6iuri7jxo2rdAxgJbmpPgAAAACFohADAAAAkiRLliypdARoEwoxAAAAaMeuu+66jB49Oj179szAgQOzyy675JVXXkmS/OQnP0ldXV169OiRTTfdNBdffHHLdM8++2yqqqpy7bXXZvz48enRo0cuueSS9OzZM7fcckurZfzqV79Knz59Wr5E4Pnnn88nP/nJ9OvXLwMGDMjee++dZ599ts3WGd4rhRgAAAC0U42NjTnwwANz2GGHpVQqpb6+Pvvtt1/K5XKuuuqqnHzyyTnzzDNTKpVy1lln5aSTTsoVV1zRah5f//rXc+yxx6ZUKmX//ffPXnvtlZ///OetnnPVVVdln332SXV1dZYsWZLddtstffr0yd13353p06end+/e2X333bN48eK2XH1YZW6qDwAAAO1UY2Njli5dmv322y/Dhw9PkowePTpJcsopp+Tcc8/NfvvtlyTZaKON8vjjj+fSSy/Npz/96ZZ5HHfccS3PSZKDDz44hxxySJqbm1NdXZ1//OMfuemmm/KrX/0qSXLttddm+fLl+clPfpKqqqokyZQpU9KvX7/U19dn1113bZN1h/dCIQYAAADt1NixY7Pzzjtn9OjR2W233bLrrrvmE5/4RLp165Y5c+bk8MMPzxFHHNHy/KVLl2adddZpNY8PfvCDrR7vueee6dq1a6ZOnZpPfepT+eUvf5m+fftml112SZLMmjUrTz/9dPr06dNqutdeey1z5sxZQ2sKq5dCDAAAANqpzp0754477si9996b22+/PT/4wQ9y4okn5je/+U2S5LLLLsuHPvShN03z73r16tXqcbdu3fKJT3wiP//5z/OpT30qP//5z3PAAQekS5fXK4SFCxdmyy23zFVXXfWmPIMGDVqdqwdrjEIMAAAA2rGqqqpst9122W677XLyySdn+PDhmT59etZbb738+c9/zsEHH7zS8zz44IPzkY98JH/605/yu9/9LmeccUbLuHHjxuXaa6/Nuuuum759+67OVYE246b6AAAA0E7NmDEjZ511Vh588ME0NDTk+uuvz0svvZS6urqcdtppOfvss3PRRRflqaeeyqOPPpopU6bkvPPOe9f57rjjjhkyZEgOPvjgbLTRRq2uMjv44INTU1OTvffeO3fffXeeeeaZ1NfX50tf+lL+8pe/rMnVhdXGFWIAAADQTvXt2ze///3vc8EFF+Qf//hHhg8fnnPPPTd77LFHkqS6ujrf//7389///d/p1atXRo8eneOOO+5d51tVVZUDDzww3/ve93LyySe3GlddXZ3f//73OeGEE7LffvtlwYIFWX/99bPzzju7Yox2QyEGAAAA7VRdXV1uvfXWtx1/0EEH5aCDDnrLcRtuuGHK5fLbTvvd73433/3ud99y3JAhQ3LFFVesXFhYi/jIJAAAAACF4goxAAAAWEWlUmmlnv/ss8/mt7/9bQ4++OD07NlzDaVq31555ZVcffXV2W233TJs2LCVmnZlfx8Ul0IMAAAAVlJNTU2qq6szadKkVZr+kksuWc2JOp5V3UbV1dWpqalZzWnoaBRiAAAAsJJqa2tTKpXS1NS0UtNNmzYtxx9/fO68887069dvzYRr51566aXsvvvuufDCC7P99tuv9PQ1NTWpra1dA8noSBRiAAAAsApqa2tXunhpaGhIkowdOzYDBw5cE7HavcbGxiTJiBEjMm7cuAqnoaNyU30AAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKF0qXQAAAAAqKRSqdRmy5ozZ06SZNasWenXr1+bLbc9eemll5IkTz/9dGbOnNlmy23L/YDKU4gBAABQSDU1Namurs6kSZPafNk777xzmy+zvTn22GPbfJnV1dWpqalp8+XS9hRiAAAAFFJtbW1KpVKampoqHYW1RE1NTWpraysdgzagEAMAAKCwamtrFSBQQG6qDwAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACF0qXSAQAAAOC9KpVKlY6wQhoaGlJfX59PfvKT6dGjR6XjrJWam5tz3XXXZaeddsoGG2xQ6TgrpL3sf/yLQgwAAIB2q6amJtXV1Zk0aVKlo6yUCy+8sNIR1nrtbRtVV1enpqam0jFYQQoxAAAA2q3a2tqUSqU0NTVVOsoKmTZtWo4//vjceeed6devX6XjrJVeeuml7L777rnwwguz/fbbVzrOCqupqUltbW2lY7CCFGIAAAC0a7W1te2miGhoaEiSjB07NgMHDqxwmrVTY2NjkmTEiBEZN25chdPQUbmpPgAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgULpUOgAAAAC8oVQqVTrCGjVnzpwkyaxZs9KvX7/KhllLvfTSS0mSp59+OjNnzqxwmjWro+/vazOFGAAAABVXU1OT6urqTJo0qdJR2sTOO+9c6QhrvWOPPbbSEdpEdXV1ampqKh2jcBRiAAAAVFxtbW1KpVKampoqHQXaVE1NTWpraysdo3AUYgAAAKwVamtrFQNAm3BTfQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAolC6VDgAAAMDaqVQqVTpCh9PpL/PS7b6n8tp/bZl071rpOGulVxcvz9RZL2eHTfpkvX7dKh2nw/G6fp1CDAAAgFZqampSXV2dSZMmVTpKh7Nrp8H5Sdetst13jsv8LKl0nLVTj3XSac+L8r1Tjk9eeKTSaTqk6urq1NTUVDpGRSnEAAAAaKW2tjalUilNTU2VjtLhdLurlHzjmvzud79LeZ3qSsdZKzUtWJI9LnoyF110UbYb0afScTqkmpqa1NbWVjpGRSnEAAAAeJPa2trC/8G8JjT/ZVHmJhkzZkw6D+xb6ThrpcaXm5M8mfe9730ZN26DSsehg3JTfQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUCgKMQAAAAAKRSEGAAAAQKEoxAAAAAAoFIUYAAAAAIWiEAMAAACgUBRiAAAAABSKQgwAAACAQlGIAQAAAFAoCjEAAAAACkUhBgAAAEChKMQAAAAAKBSFGAAAAACFohADAAAAoFAUYgAAAAAUikIMAAAAgEJRiAEAAABQKAoxAAAAAApFIQYAAABAoSjEAAAAACgUhRgAAAAAhaIQAwAAAKBQFGIAAAAAFIpCDAAAAIBCUYgBAAAAUChV5XK5XOkQ8G5+98QTGTl4cDbo37/SUdZaDz1zf6q790rdeu+vdBSA1eb5Bx/Na39fmE123qbSUdZaf54/Nw+98NfsP2pMpaMAsAKW/nVuFv+hlJ57fShV3btWOs5a6dVFS3PjQ3/J9nXrZmj/6krHoYNSiAEAAABQKD4yCQAAAEChKMQAAAAAKBSFGAAAAACFohAD+P/bsQMBAAAAAEH+1oNcGAEAALAixAAAAABYEWIAAAAArAgxAAAAAFaEGAAAAAArQgwAAACAFSEGAAAAwIoQAwAAAGBFiAEAAACwIsQAAAAAWBFiAAAAAKwIMQAAAABWhBgAAAAAK0IMAAAAgBUhBgAAAMCKEAMAAABgRYgBAAAAsCLEAAAAAFgRYgAAAACsCDEAAAAAVoQYAAAAACtCDAAAAIAVIQYAAADAihADAAAAYEWIAQAAALAixAAAAABYEWIAAAAArAgxAAAAAFaEGAAAAAArQgwAAACAFSEGAAAAwIoQAwAAAGBFiAEAAACwIsQAAAAAWBFiAAAAAKwIMQAAAABWhBgAAAAAK0IMAAAAgBUhBgAAAMCKEAMAAABgRYgBAAAAsCLEAAAAAFgRYgAAAACsCDEAAAAAVoQYAAAAACtCDAAAAIAVIQYAAADAihADAAAAYEWIAQAAALAixAAAAABYEWIAAAAArAgxAAAAAFaEGAAAAAArQgwAAACAFSEGAAAAwIoQAwAAAGBFiAEAAACwIsQAAAAAWBFiAAAAAKwIMQAAAABWhBgAAAAAK0IMAAAAgBUhBgAAAMCKEAMAAABgRYgBAAAAsCLEAAAAAFgRYgAAAACsCDEAAAAAVoQYAAAAACtCDAAAAIAVIQYAAADAihADAAAAYCWgVxfSFR5CVgAAAABJRU5ErkJggg=="/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 12, 
+ You 'll be hammered to a pulp Pen . No one has yet beaten Darrin at a fist fight . There always has to be a first time you know .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 13, 
+ And you think you 're It . As far as Darrin is concerned yes . Too bad too bad sighed Hallam .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 14, 
+ Then we can come back after the others . That 's agreeable to me nodded Midshipman Decker . In twenty minutes the two seconds were back .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 15, 
+ Dave still used his footwork and most cleverly . Yet wherever he went Pen followed him nimbly . It did n't look so one sided now .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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+</div>
+</div>
+<div class="jp-OutputArea-child">
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+<pre> sample -&gt; 16, 
+ But Pennington to whom he appealed shook his head . Too big a risk Hally replied Pen . And trebly dangerous with that greaser Darrin in the class .
+</pre>
+</div>
+</div>
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+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 17, 
+ unk the three middies stepped inside the darkened shed . Suddenly the door banged and a padlock clicked outside . Here stop that you rascally joker roared Dalzell wheeling about .
+</pre>
+</div>
+</div>
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+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 18, 
+ We 're sure enough idiots groaned Dan . Hold on there fellows interrupted Dave Darrin . unk all your anger right on me .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 19, 
+ No retorted Dave shaking his head . We 've seen the uselessness of asking help from outside . Let 's supply our own help .
+Skipping line 20 due to an error: Bobcat failed to parse "We ' ve seen the uselessness of asking help from outside .".
+ sample -&gt; 20, 
+ Farley took hold at the further end . Up with it heaved Midshipman Darrin . Right up the spar went .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 21, 
+ It 's all up with our summer 's fun . Is it though shouted Dave spurting ahead . Come on and find out .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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my7jm2z+6hdctpvz1yhLLZl0P+cMkklurq60t3d/Yqf/8DtHfnpf66fQz5xb4YMfeUjXK/XW+r6ViuyXhc9/mj2/f43M2OXydluvQ1XKEcrrdcVndU/Tf63DD/wzRnxnretUI5WWqcwEK3o9/ovLu3Or7//cA64YPwK5Wil7/UVXae/faYr//nkt/Pe9vdl1KC1X/FyWmmdAqveim7Lllz7v1ky+/HUZqxYmWVbBv3PEWJUotForNgG/ZEl+WmeyNYTJ2boGm39F2w1tyLrdfRD3cn3k/Hjx2fSazv7Odnqa0Vn9fqhw7LB2A3yWtd7gAFtRb/XH/7Jz7Nw6GOu7fJXVnSdtj/Snvw82Xzz12e94ev1YzKA5bei27Lf/+o3+c2ge+wfYADyWyYBAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKMqQqgMMBF1dXenu7q46Bi/DAws7kqyf2xcsyJChy6qO0xIWPf5okuSee+7J2o/+b8VpWseSJU9l0eJFeXT+/Kqj8ArU6/U0Go0VWoZ9TBkWLerOkiVLMt/3er/57TNdSZJf/OLn+f2g31echlXBNpdWtOS3/5ulS5faPzDg9Mc2d3XXtmzZsqLbhK6urnR2dqanp6fqKLwMQwa1Z40ha+Xxp/5YdZTW0daW1NqTJ55MnlladZqWMSpD82Seyf/GOl0d1Wq1NJvNV/xhwT6mHEPb2jO0bVh6lj5adZSWMWjooAwbOSxPPvJklj1T9MfVYtjm0oraB62R2qBaHnr64aqjQB8rus1tBcUfIdbd3Z2enp7MmjUrnZ2dVccBYIBoNpuZMmVKuru7X/EHBfsYgOVjmwuw6vTHNrcVFF+IPaezszOTJk2qOgYALcg+BmDVsc0FYHm4qD4AAAAARVGIAQAAAFAUhRgAAAAARVGIAQAAAFAUhRgAAAAARVGIAQAAAFAUhRgAAAAARVGIAQAMcFOnTs3+++9fdYyXZXXMDFClmTNnZtSoUVXHgGIoxKBgU6dOzfTp06uOAcAAocQCAEqhEAMAAACgKAoxIElyzjnnZPz48Wlvb896662Xgw8+uOpIAMW59NJLs8UWW2T48OFZZ511sscee+Txxx/v/foZZ5yRsWPHZp111sk///M/Z8mSJb1fe/jhh3PkkUdm7bXXTq1Wy9ve9rbcc889vV+fPn16Jk6c2Of1vvjFL2bcuHG9X7/wwgvz3e9+N21tbWlra8vcuXOTJL/97W9z6KGHZtSoURk9enT222+/PPDAA73LeeaZZ3L88cdn1KhRWWeddfLhD384y5Yt6/f1AzBQXH311dlpp516t3uTJ0/OfffdlyR54IEH0tbWltmzZ2e33XZLrVbLVlttlZ/+9Kd9ljFz5sw0Go3UarUccMAB+fOf/1zFW4FiKcSAzJs3L8cdd1w+8YlP5O67787VV1+dnXfeuepYAEVZvHhxDj/88LznPe9Js9nM3Llzc+CBB/YWSz/84Q9z33335Yc//GEuvPDCzJw5MzNnzux9/tSpUzNv3rxcccUV+elPf5ply5Zl77337lOavZgTTzwxhx56aPbaa68sXrw4ixcvzpve9KYsWbIke+65Zzo6OvLjH/84N910U0aMGJG99torTz31VJLkzDPPzMyZM3P++efnxhtvzEMPPZTvfOc7/b6OAAaKxx9/PMcff3zmzZuX6667LoMGDcoBBxyQpUuX9j7mYx/7WE488cQsWLAgm266aQ4//PA8/fTTSZKbb745733ve3PsscdmwYIF2W233fKpT32qqrcDRRpSdQCgOs/9IDV79uysueaamTx5cjo6OrLxxhtn6623rjYcQGEWL16cp59+OgceeGA23njjJMkWW2zR+/W11147X/7ylzN48OC87nWvyz777JPrrrsu06ZNyz333JMrrrgiN910U970pjclSS6++OJstNFGufzyy3PIIYe85OuPGDEiw4cPz5NPPpn111+/9/5Zs2Zl6dKl+drXvpa2trYkyQUXXJBRo0Zl7ty5+Yd/+Id88YtfzEc/+tEceOCBSZJzzz03P/jBD/pt3QAMNAcddFCf2+eff37GjBmTX/7ylxkxYkSSZ/+jYZ999kmSnHLKKdl8881z77335nWve13OOuus7LXXXvnwhz+cJNl0003zk5/8JFdfffWqfSNQMEeIAXnrW9+ajTfeOK95zWvyzne+MxdffHF6enqqjgVQlK222iq77757tthiixxyyCE577zz8vDDD/d+ffPNN8/gwYN7b48dOzYPPvhgkqTZbGbIkCF54xvf2Pv1ddZZJ5tttlmazeYK5brjjjty7733pqOjIyNGjMiIESMyevToPPHEE7nvvvvyP//zP1m8eHGf1x4yZEi23XbbFXpdgIHsnnvuyeGHH57XvOY1GTlyZO/p511dXb2P2XLLLXv/Pnbs2CTps93+6+1mkuywww4rOTXw1xRiQDo6OjJ//vxccsklGTt2bE4++eRstdVWeeSRR6qOBlCMwYMH59prr81VV12VCRMm5Etf+lI222yzLFy4MEkydOjQPo9va2vrc2rOSxk0aNDzruu1PKdTPvbYY9lmm22yYMGCPn9+/etf5x3veMdyvz5AK9l3333z0EMP5bzzzsvNN9+cm2++OUl6TyVP+m63nzvC9uVst4GVSyEGJHn2f/P32GOPfPazn82dd96ZBx54INdff33VsQCK0tbWlh133DGnnHJKbr/99gwbNmy5rsXV2dmZp59+uvcHsiT585//nLvvvjsTJkxIkowZMyZ/+MMf+pRiCxYs6LOcYcOG5Zlnnulz36RJk3LPPfdk3XXXzWtf+9o+f9Zaa62stdZaGTt2bJ/Xfvrpp3Pbbbe9klUAMOA9t3096aSTsvvuu6ezs7PPEb3Lo7Ozs892M0l+9rOf9WdM4CUoxIBceeWVOfvss7NgwYL85je/yTe+8Y0sXbo0m222WdXRAIpx880359RTT828efPS1dWV2bNn509/+lM6Oztf8rnjx4/Pfvvtl2nTpuXGG2/MHXfckSlTpuRVr3pV9ttvvyTJrrvumj/96U/57Gc/m/vuuy9f+cpXctVVV/VZzrhx43LnnXfm7rvvTnd3d5YsWZIjjjgi9Xo9++23X3784x9n4cKFmTt3bo477rj87ne/S5L8y7/8S04//fRcfvnl+dWvfpX3v//9jjIGWtbaa6+dddZZJ//xH/+Re++9N9dff32OP/74l7WM4447LldffXXOOOOM3HPPPfnyl7/s+mGwiinEgIwaNSqzZ8/OW97ylnR2dubcc8/NJZdcks0337zqaADFGDlyZH70ox9l7733zqabbpqTTjopZ555Zt72trct1/MvuOCCbLPNNpk8eXJ22GGHLFu2LHPmzOk9ZaezszPnnHNOvvKVr2SrrbbKLbfckhNPPLHPMqZNm5bNNtss2267bcaMGZObbroptVotP/rRj9JoNHLggQems7Mz733ve/PEE09k5MiRSZITTjgh73znO/Oud70rO+ywQzo6OnLAAQf07woCGCAGDRqUb33rW7ntttvy+te/Ph/84Afzuc997mUtY/vtt895552Xs846K1tttVWuueaanHTSSSspMfBC2pb97cUkCjN//vxss802ue222zJp0qSq4wAwQPTH/sE+BmD52OYCrDq2l89yhBgAAAAARVGIAQAAAFAUhRgAAAAARVGIAQAAAFAUhRgAAAAARVGIAQAAAFAUhRgAAAAARVGIAQAAAFCUIVUHaBVdXV3p7u6uOgYA/6der6fRaFQdo1/Yx/BiVtWsm0NejG0upbDNZSBopW1ulRRi/aCrqyudnZ3p6empOgoA/6dWq6XZbK72HxbsY3gpq2LWzSEvxTaXUtjmMhC0yja3agqxftDd3Z2enp7MmjUrnZ2dVccpwpw5c/Lxj388N910U9rb26uO0xIWLVqUfffdNzNmzMh2221XdZyWseeee+bggw/OtGnTqo5SlGazmSlTpqS7u3u1/6BgH9PXIYccku233z4nnHBC1VEGhFU16+awL9v2vmxzW9fs2bNz2mmn5dZbb606yoBgm1uNCy64IBdddFGuv/76qqMMCK20za2aQqwfdXZ2ZtKkSVXHKEKz2UySbL311hk+fHjFaVrD6NGjkyTjx483x/1o6NCh2WCDDaxTVph9zLPa29uz7rrrWhcVMYfPsm1vfWb9WfPmzUsS66Ii5vBZ11xzTYYMGWJd0O9cVB8AAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACjKkKoD8P/r6upKd3d31TFWCwsXLkyS3H777Wlvb684TWtYtGhRkuSee+7J2muvXXGa1rFkyZIsWrQo8+fPrzrKaqNer6fRaFQdo+W0yj7miSeeyIMPPtgy31OlzXurzGGrbdtLm8NVoVVmvaurK0nM+mqqVebw97//fZ5++mlzSL9TiA0QXV1d6ezsTE9PT9VRVis77rhj1RFazjHHHFN1hJZz7rnn5txzz606xmqjVqul2Wz6oNCPWm0fc//99+eb3/xm1TH6RUnz3mpz2Erb9pLmcFVotVlPkm222abqCP2ipFk3hwNXSXM40CnEBoju7u709PRk1qxZ6ezsrDoOQCWazWamTJmS7u5uHxL6kX3MwFTavJvDgam0OVwVzPrAVNqsm8OBqbQ5HOgUYgNMZ2dnJk2aVHUMAFqQfQwDgTmkFGadgcAcwt/novoAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBvTxwAMPpK2tLQsWLEiSzJ07N21tbXnkkUcqzQWs3gbStmTmzJkZNWpU1TEYQP5237cqDaTvDQAoiUIMAOh3u+66az7wgQ9UHQMAaHHTp0/PxIkTq47BakghBgAAAEBRFGJQoKuvvjo77bRTRo0alXXWWSeTJ0/OfffdV3UsoEVMnTo1N9xwQ84666y0tbWlra0tDzzwQJLktttuy7bbbptarZY3velNufvuu/s8d8aMGdlkk00ybNiwbLbZZrnooot6v/ZCp7U98sgjaWtry9y5c3vvu+KKKzJ+/Pi0t7dnt912y4UXXviCp6T94Ac/SGdnZ0aMGJG99torixcv7u9VwQCyPPu++++/P7vttltqtVq22mqr/PSnP+3z9RtvvDFvfvObM3z48Gy00UY57rjj8vjjj/d+/aKLLsq2226bjo6OrL/++nnHO96RBx98sM8y5syZk0033TTDhw/Pbrvt1vu9AVV68sknc9xxx2XddddNe3t7dtppp9x6661J/v/Teq+77roX3X7DK/Vi8/dClzm4/PLL09bW1vv1U045JXfccUfvZ46ZM2eu4nfA6kohBgV6/PHHc/zxx2fevHm57rrrMmjQoBxwwAFZunRp1dGAFnDWWWdlhx12yLRp07J48eIsXrw4G220UZLkYx/7WM4888zMmzcvQ4YMyXve857e533nO9/Jv/zLv+SEE07Iz3/+8xx11FF597vfnR/+8IfL/doLFy7MwQcfnP333z933HFHjjrqqHzsYx973uN6enpyxhln5KKLLsqPfvSjdHV15cQTT1zxN8+AtTz7vo997GM58cQTs2DBgmy66aY5/PDD8/TTTydJ7rvvvuy111456KCDcuedd+bb3/52brzxxhx77LG9z1+yZEk++clP5o477sjll1+eBx54IFOnTu39+m9/+9sceOCB2XfffbNgwYK8733vy7/+67+usnUAf8+HP/zhXHbZZbnwwgszf/78vPa1r82ee+6Zhx56qPcxL7b9hhWxPPP39xx22GE54YQTsvnmm/d+5jjssMNWQWpawZCqAwCr3kEHHdTn9vnnn58xY8bkl7/8ZUaMGFFRKqBVrLXWWhk2bFhqtVrWX3/9JMmvfvWrJMmnP/3p7LLLLkmSf/3Xf80+++yTJ554Iu3t7TnjjDMyderUvP/970+SHH/88fnZz36WM844I7vttttyvfZXv/rVbLbZZvnc5z6XJNlss83y85//PJ/+9Kf7PG7JkiU599xzs8kmmyRJjj322HziE59Y8TfPgLU8+74TTzwx++yzT5LklFNOyeabb5577703r3vd63LaaafliCOO6L023vjx43P22Wdnl112yYwZM9Le3t6nIHjNa16Ts88+O294wxvy2GOPZcSIEb1HQJ555plJnp3Pu+66K5/5zGdWwRqAF/b4449nxowZmTlzZt72trclSc4777xce+21+frXv543vOENSV58+w2v1EvN35gxY170+cOHD8+IESMyZMiQ3s8csLwcIQYFuueee3L44YfnNa95TUaOHJlx48YlSbq6uqoNBrS8LbfcsvfvY8eOTZLeU8qazWZ23HHHPo/fcccd02w2l3v5d999d+8Pb8/Zbrvtnve4Wq3WW4Y9l+VvT22jtSzPvu/F5vOOO+7IzJkzM2LEiN4/e+65Z5YuXZqFCxcmefaU4H333TeNRiMdHR295cFzr9FsNvPGN76xT64ddthh5bxhWE733XdflixZ0mf7O3To0Gy33XZ9tr8v9v0Br9Tyzh+sDI4QgwLtu+++2XjjjXPeeedlgw02yNKlS/P6178+Tz31VNXRgBY3dOjQ3r8/d/2P5T1de9CgZ/8fb9myZb33LVmyZIVzPJflr5dL61mefd+Lzedjjz2Wo446Kscdd9zzlt1oNPL4449nzz33zJ577pmLL744Y8aMSVdXV/bcc0/7V1rCimy/4ZUaNGjQ8/bPr3TfD3/LEWJQmD//+c+5++67c9JJJ2X33XdPZ2dnHn744apjAS1m2LBheeaZZ17Wczo7O3PTTTf1ue+mm27KhAkTkqT3tIm/vvj9X19gP3n2FLR58+b1ue+5C/NSrv7Y902aNCm//OUv89rXvvZ5f4YNG5Zf/epX+fOf/5zTTz89b37zm/O6173ueUfPdHZ25pZbbulz389+9rMVfn+wIp77RSZ/vf1dsmRJbr311t7tL6wsLzV/Y8aMyV/+8pc+v8Dkb/f9r+QzBySOEIPirL322llnnXXyH//xHxk7dmy6urpc0Bfod+PGjcvNN9+cBx54ICNGjFiuowg+9KEP5dBDD83WW2+dPfbYI9/73vcye/bs/Pd//3eSZ68Tsv322+f000/Pq1/96jz44IM56aST+izjqKOOyuc///l85CMfyXvf+94sWLCg97dNPXdEA+Xpj33fRz7ykWy//fY59thj8773vS9rrrlmfvnLX+baa6/Nl7/85TQajQwbNixf+tKXcvTRR+fnP/95PvnJT/ZZxtFHH50zzzwzH/rQh/K+970vt912m9+GRuXWXHPNHHPMMfnQhz6U0aNHp9Fo5LOf/Wx6enry3ve+N3fccUfVEWlhLzV/y5YtS61Wy7/927/luOOOy8033/y87ea4ceOycOHCLFiwIBtuuGE6OjqyxhprVPOGWK04QgwKM2jQoHzrW9/Kbbfdlte//vX54Ac/2HvxaYD+cuKJJ2bw4MG9/7u7PNco3H///XPWWWfljDPOyOabb56vfvWrueCCC7Lrrrv2Pub888/P008/nW222SYf+MAH8qlPfarPMl796lfn0ksvzezZs7PllltmxowZvb9l0ofjcvXHvm/LLbfMDTfckF//+td585vfnK233jonn3xyNthggyTPHsE4c+bM/Nd//VcmTJiQ008/PWeccUafZTQajVx22WW5/PLLs9VWW+Xcc8/Nqaee2m/vE16p008/PQcddFDe+c53ZtKkSbn33nvzgx/8IGuvvXbV0SjAi83f6NGjM2vWrMyZMydbbLFFLrnkkkyfPr3P8w866KDstdde2W233TJmzJhccskl1bwRVjuOEIMC7bHHHvnlL3/Z576/Pjf/r/++6667uq4O8LJtuumm+elPf9rnvqlTp/a5PXHixOdtX4455pgcc8wxf3e5nZ2d+clPftLnvr9dxtvf/va8/e1v77396U9/OhtuuGHvb0KbOnXq87Lsv//+tnUt7uXs+5Jk1KhRz7vvDW94Q6655pq/+xqHH354Dj/88L/7GkkyefLkTJ48uc997373u1/6DcBK1N7enrPPPjtnn3328772Qp8FX2j7Da/Ui81f8uw+ev/99+9z37Rp03r/vsYaa+TSSy9dmRFpUQoxAKClnHPOOXnDG96QddZZJzfddFM+97nP5dhjj606FgAAA4hCDABoKffcc08+9alP5aGHHkqj0cgJJ5yQj370o1XHAgBgAFGIAQAt5Qtf+EK+8IUvVB0DAIABzEX1AQAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiK3zLZYrq6utLd3V11DKBQ9Xo9jUaj6hisJPYxfZn3apjDvsxh6zLrfZn1apjDvsxha1GItZCurq50dnamp6en6ihAoWq1WprNpg8KLcg+5vnM+6pnDp/PHLYms/58Zn3VM4fPZw5bi0KshXR3d6enpyezZs1KZ2dn1XFWqjlz5uTjH/94brrpprS3t1cdpyUsWrQo++67b2bMmJHtttuu6jgtY88998zBBx+cadOmVR1lpWs2m5kyZUq6u7t9SGhBA2Ufc8ghh2T77bfPCSecUFmGxLxXZaDM4UDZtpvD1jVQZn327Nk57bTTcuutt1aWITHrVRkoc3jBBRfkoosuyvXXX19ZhsQctiKFWAvq7OzMpEmTqo6xUjWbzSTJ1ltvneHDh1ecpjWMHj06STJ+/PiWn59VaejQodlggw2sU1pG1fuY9vb2rLvuur6nClf1HNq2s6pUPevz5s1LErNeuKrn8JprrsmQIUPMIf3ORfUBAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiDKk6AANPV1dXuru7q47xohYtWpR11lknt99+e9rb26uO0xL+8Ic/ZJ111skDDzyQ+fPnVx2nZXR0dOSRRx5ZLdZpvV5Po9GoOgYtbkX3Me3t7XnsscdW+HvKvJdtReewv7bt5pCVbUVn/Y9//GPWWWcds84KWdE5fOihh7LWWmuZQ/qdQow+urq60tnZmZ6enqqjLJcdd9yx6ggt533ve1/VEVrO5z//+Xz+85+vOsZLqtVqaTabPiiw0vTXPmb+/Pn52te+tkLLMO/l6q857I9tuzlkZerPz/XbbLPNCj3frJfLHDKQKcToo7u7Oz09PZk1a1Y6OzurjgOsIs1mM1OmTEl3d7cPCaw0A2UfY97LZg4phVlnIDCHDGQKMV5QZ2dnJk2aVHUMAFqQfQwDwcqew1133TUTJ07MF7/4xZX2GrA8bHMZCP52DpctW5ajjjoql156aR5++OGstdZamTp16gptMx944IG8+tWvzu23356JEyeueGhankIMABjQpk6dmkceeSSXX3551VHgeebOnZvddtstDz/8cEaNGtV7/+zZszN06NDqgsH/+exnP5tbbrklv//977Puuutm4sSJ+cAHPpDdd9+96mgU7Oqrr87MmTMzd+7cvOY1r8mgQYMyfPjwqmNRGIUYAAC8AkuWLPm7Xxs9evQqTALPt2jRoiTJrbfems997nPZYostsmTJkvzgBz/IP//zP+dXv/pVxQkp2X333ZexY8fmTW96U9VRKNigqgMAACyvSy+9NFtssUWGDx+eddZZJ3vssUcef/zxqmMxQPy9+Zg6dWr233//nHLKKb1HxZx66ql56qmnep87bty4552qM3HixEyfPr33dltbW2bMmJG3v/3tWXPNNTNt2rTstttuSZK11147bW1tmTp1apJnT5n8wAc+0Gf5p556at7znveko6Mje++99/Py/+QnP8nEiRPT3t6ebbfdNpdffnna2tqyYMGCflk/lOX0009PknzjG9/IQQcdlE033TSbb755jj/++PzsZz9L8uwFz/fbb7+MGDEiI0eOzKGHHpo//vGPvcuYPn16Jk6cmPPPPz+NRiMjRozI+9///jzzzDP57Gc/m/XXXz/rrrtuPv3pT/d57UceeSTve9/7MmbMmOy8885Jkl//+ter6J0z0E2dOjX/7//9v3R1daWtrS3jxo17yW1mo9HIf/zHf/RZzi233JKtt966d5t5++23r+J3wupOIQYArBYWL16cww8/PO95z3vSbDYzd+7cHHjggVm2bFnV0RgAXmo+rrvuujSbzd4fqK6//vqccsopL/t1pk+fngMOOCB33XVXTjnllFx22WVJkrvvvjuLFy/OWWed9Xefe+aZZ/b+0HbIIYckefaaN0ny6KOPZt99980WW2yR+fPn55Of/GQ+8pGPvOx8kCQPPfRQfvKTnyTJC56GNmrUqCxdujT77bdfHnroodxwww259tprc//99+ewww7r89j77rsvV111Va6++upccskl+frXv5599tknv/vd73LDDTfkM5/5TE466aTcfPPNvc855JBD8uCDD+aqq67KrFmzkiRHH310HnrooZX4rlldnHXWWfnEJz6RDTfcMIsXL86tt976go/7623m+9///hxzzDG5++67kySPPfZYJk+enAkTJuS2227L9OnTc+KJJ67Kt0ELcMokALBaWLx4cZ5++ukceOCB2XjjjZMkW2yxRcWpGCheaj6GDRuW888/v/c0saOPPjpnn312PvnJT2bQoOX/P+J3vOMdefe73917e+HChUmSddddt881xF7I3nvvnfe///1Jnj1C4stf/nLmzZuXAw88MN/85jfT1taW8847L+3t7ZkwYUJ+//vfZ9q0acudDZ5z7733vuR/Flx33XW56667snDhwmy00UZJnj2abPPNN8+tt96aN7zhDUmSpUuX5vzzz09HR0cmTJiQ3XbbLXfffXfmzJmTQYMGZbPNNstnPvOZ/PCHP8wb3/jG3Hjjjbnlllvy4IMPZo011uj9/uro6Mill16af/qnf1q5b54Bb6211kpHR0cGDx6c9ddf/+8+7q+3mR/5yEfyhS98IT/84Q+z2Wab5Zvf/GaWLl2ar3/962lvb8/mm2+e3/3udznmmGNW1dugBThCDABYLWy11VbZfffds8UWW+SQQw7Jeeedl4cffrjqWAwQLzUfW221VWq1Wu/tLbfcMo899lh++9vfvqzX2XbbbV9xxi233LL3721tbUnSe8TM3XffnS233DLt7e29j9luu+1e8WtRtuU5crbZbGajjTbqLcOSZMKECRk1alSazWbvfePGjUtHR0fv7fXWWy8TJkzoUySvt956efDBB5Mkd9xxRx577LGss846GTFiRHbaaackz17T7L777lvh90Y5/nabuf766/fOWbPZfN42c4cddljlGVm9KcQAgNXC4MGDc+211+aqq67KhAkT8qUvfSmbbbZZ7xE6lG1F52PQoEHPKxFe6KL5a6655ivO+EK/ddIpv6wM48eP7y1dV9Tfzm1bW9sL3rd06dIkz57KNnbs2CxYsCALFizIJZdckuTZ37z6oQ99qF8yUYYXmzPoDwoxAGC10dbWlh133DGnnHJKbr/99gwbNizf+c53qo7FAPFi83HHHXfkf//3f3sfe9ddd2XEiBG9R8eMGTMmixcv7v36o48+ulxl2rBhw5IkzzzzzApl32yzzXLXXXflySef7L3v711XB17K6NGje4+W+eu5f84jjzySzs7O/Pa3v+1zlOQvf/nLPPLII5kwYcIrfu1JkyblD3/4Q4YMGZLXvva1vd9jG220Uer1+iteLvy1zs7O3HnnnXniiSd673vul0XA8lKIAQCrhZtvvjmnnnpq5s2bl66ursyePTt/+tOf0tnZWXU0BoCXmo+nnnoq733ve3P//fcnSb761a/m2GOP7T3t6y1veUsuuuii/PjHP85dd92Vd73rXRk8ePBLvu7GG2+ctra2XHnllfnTn/6Uxx577BXlf8c73pGlS5fmn/7pn9JsNvODH/wgZ5xxRpL025E+lOW5X8pw5JFH5rLLLss999yTZrOZs88+OzvssEP22GOPbLHFFjniiCMyf/783HLLLTnyyCOzyy67rNCpwXvssUd22GGH7L///rnmmmuyaNGiJMlXvvKVzJs3r1/eG7zjHe9IW1tbpk2bll/+8peZM2dO7zYTlpdCDABYLYwcOTI/+tGPsvfee2fTTTfNSSedlDPPPDNve9vbqo7GAPBS87H77rtn/Pjxed/73pck2XnnnTN9+vTe53/0ox/NLrvsksmTJ2efffbJ/vvvn0022eQlX/dVr3pVTjnllPzrv/5r1ltvvRx77LGvOP/3vve9LFiwIBMnTszHPvaxnHzyyUnS5xo5sLw23HDDJM9e9+6EE07I61//+rz1rW/NddddlxkzZqStrS3f/e53s/baa2fnnXfOHnvskde85jX59re/vUKv29bWljlz5mTnnXfOu9/97hxwwAFJnv3FF+utt94Kvy9IkhEjRuR73/te7rrrrmy99db52Mc+ls985jNVx2I147dMQmHGjRuXD3zgA/nABz7Qe9/EiROz//77Z/r06Wlra8s555yTK664InPnzs3YsWPz2c9+NgcffHB1oYGizZw5s/fvV199dXVBGNA6Oztfcj5OOeWU7Lffftlmm21y0kknZY011uj92siRI/Otb32rz+Pf9a539bn996739fGPfzwf//jH+9w3d+7cPrcfeOCBF3zuUUcd1fv3N73pTbnjjjt6b1988cUZOnRoGo3G331P8FI+8pGPZNKkSS/4tUajke9+97t/97nTp0/vUxwnfbfJz/nbee/o6MjZZ5+ds88+O/Pnz88222yTT33qU30u4E/Z/vbnkeXZZi5YsKDP7e233/5597kuIy+HI8SA5/n4xz+egw46KHfccUeOOOKI/OM//mOf3zYEAPS/b3zjG7nxxhuzcOHCXH755fnIRz6SQw89NMOHD686GgC0HEeIAc9zyCGH9J5S8slPfjLXXnttvvSlL+Wcc86pOBnQClakYP/BD36Q9dZbLxMnTqzk9WkdKzIH3/ve97LJJpus0IXHX+j1//CHP+Tkk0/OH/7wh4wdOzaHHHJIPv3pT7/i14BkxWb9/vvvz/z581foTAHbXJIVm4Nms5l77rknb3/72yt5fVqXQgx4nud+K9Ff3/7bw5EBXq56vZ5arZYpU6ZUHSW1Ws1vOyvIX5/iNZDn8MMf/nA+/OEPV5iIVtKfs37aaaet0PNtc8vVn3N4yimnrNDzzSF/SyEGhRk0aNDzzq1fsmRJRWmAkjQajTSbzXR3d7/iZRxyyCHZfvvtc8IJJ6xQlnq97rpMheqPOdxzzz1z8MEHZ9q0aSuUxRyyMvXHrM+ePTunnXZabr311hXKYtbL1R9zeMEFF+Siiy7K9ddfv0JZzCF/SyEGhRkzZkwWL17ce/vRRx/NwoUL+zzmZz/7WY488sg+t7feeutVlhFoXY1GY4U+jLa3t2fdddf9uxeIhuWxonM4dOjQbLDBBuaQAW9FZ33evHlJYtZZISs6h9dcc02GDBliDul3CjEozFve8pbMnDkz++67b0aNGpWTTz45gwcP7vOY//qv/8q2226bnXbaKRdffHFuueWWfP3rX68oMQAAAPQvhRgU5qMf/WgWLlyYyZMnZ6211sonP/nJ5x0hdsopp+Rb3/pW3v/+92fs2LG55JJLVujCwQAAADCQKMSgMCNHjsy3vvWtPve9613v6nN7gw02yDXXXLMqYwEAAMAqM6jqAAAAAACwKinEAAAAACiKUyaBPpYtW1Z1BAAAAFipHCEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFH8lklWiq6urnR3d1cdA4pRr9fTaDSqjgEAALBaUIjR77q6utLZ2Zmenp6qo0AxarVams2mUgwAAGA5KMTod93d3enp6cmsWbPS2dlZdZzKHX300Rk9enROPfXUqqO0jHPOOSdz5szJlVdeWXWUAaHZbGbKlCnp7u5WiAEAACwHhRgrTWdnZyZNmlR1jMp1dHRk9OjR1kU/Gjt2bIYNG2adAgAA8Iq4qD4AAAAARVGIAQAAAFAUhRgAAAAARVGIAQAAAFAUF9UHAJZbs9ms9PWfeOKJPPjgg5k/f36lOapeD6Wrev0vWbIkixYtMoesdFX/G3d1dSWJWS9c1ev/97//fZ5++mlzSL9TiAEAL6ler6dWq2XKlClVR8n999+fb37zm1XHSK1WS71erzpGUQbSHJ577rk599xzq45hDlvUQJr1JNlmm22qjmDWK2AOn88cthaFGADwkhqNRprNZrq7u6uOMmDU6/U0Go2qYxTFHD6fOWxNZv35zPqqZw6fzxy2FoUYALBcGo2GD4FUzhxSCrPOQGAOaWUuqg8AAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRlSNUB4O/p6upKd3d31TFW2KhRo9Le3p758+dXHaVlLFmyJBtuuGHLrNN6vZ5Go1F1DAAAgGIoxBiQurq60tnZmZ6enqqj9JsLLrig6ggtZ5tttqk6Qr+o1WppNptKMQAAgFVEIcaA1N3dnZ6ensyaNSudnZ1Vx4GVptlsZsqUKenu7laIAQAArCIKMQa0zs7OTJo0qeoYAAAAQAtxUX0AAAAAiqIQAwAAAKAoCjEAAAAAiqIQAwAAAKAoCjEAAAAAiqIQAwAAAKAoCjEo3MyZMzNq1KiXfFxbW1suv/zylZ4HAAAAVjaFGBTusMMOy69//eve29OnT8/EiROrCwQAAAAr2ZCqAwDVGj58eIYPH151DAAAAFhlHCEGLejKK6/MqFGj8swzzyRJFixYkLa2tvzrv/5r72Pe9773ZcqUKX1OmZw5c2ZOOeWU3HHHHWlra0tbW1tmzpzZ+5zu7u4ccMABqdVqGT9+fK644opV+bYAAACgXyjEoAW9+c1vzl/+8pfcfvvtSZIbbrgh9Xo9c+fO7X3MDTfckF133bXP8w477LCccMIJ2XzzzbN48eIsXrw4hx12WO/XTznllBx66KG58847s/fee+eII47IQw89tCreEgAAAPQbp0xCC1prrbUyceLEzJ07N9tuu23mzp2bD37wgznllFPy2GOP5X/+539y7733ZpdddslNN93U+7zhw4dnxIgRGTJkSNZff/3nLXfq1Kk5/PDDkySnnnpqzj777Nxyyy3Za6+9Vtl7A165ZrNZdQT+Sqn/HqW+74HKv8fKY90OLKX+e5T6vgcq/x4Di0IMWtQuu+ySuXPn5oQTTsiPf/zjnHbaafnP//zP3HjjjXnooYeywQYbZPz48X0KsZey5ZZb9v59zTXXzMiRI/Pggw+ujPhAP6rX66nVapkyZUrVUfgbtVot9Xq96hirhDkcuEqaw1XBrA9cJc26ORy4SprDgU4hBi1q1113zfnnn5877rgjQ4cOzete97rsuuuumTt3bh5++OHssssuL3uZQ4cO7XO7ra0tS5cu7a/IwErSaDTSbDbT3d1ddZQVdsghh2T77bfPCSecUHWUflGv19NoNKqOsUq00hzuueeeOfjggzNt2rSqo/SLkuZwVWilWZ89e3ZOO+203HrrrVVH6RclzXorzeEFF1yQiy66KNdff33VUfpFSXM40CnEoEU9dx2xL3zhC73l16677prTTz89Dz/88N/9YXLYsGG9F+MHWkej0WiJD1/t7e1Zd911M2nSpKqj8Aq0yhwOHTo0G2ywgTnk72qVWZ83b16SmPXVVKvM4TXXXJMhQ4aYQ/qdi+pDi1p77bWz5ZZb5uKLL+69eP7OO++c+fPn59e//vXfPUJs3LhxWbhwYRYsWJDu7u48+eSTqzA1AAAArHwKMWhhu+yyS5555pneQmz06NGZMGFC1l9//Wy22WYv+JyDDjooe+21V3bbbbeMGTMml1xyySpMDAAAACufUyahhX3xi1/MF7/4xT73LViwoM/tqVOnZurUqb2311hjjVx66aXPW9ayZcued98jjzzSDykBAABg1XKEGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFGVJ1AFiZurq60t3dXXUMWli9Xk+j0ag6BgAAAC+DQoyW1dXVlc7OzvT09FQdhRZWq9XSbDaVYgAAAKsRhRgtq7u7Oz09PZk1a1Y6Ozsry3H00Udn9OjROfXUUyvL0GrOOeeczJkzJ1deeWWlOZrNZqZMmZLu7m6FGAAAwGpEIUbL6+zszKRJkyp7/Y6OjowePbrSDK1m7NixGTZsmHUKAADAK+Ki+gAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFFcVB8AVgPNZrPqCAPCE088kQcffDDz58+vOsqAsKrnwhw+a8mSJVm0aJE5/D+tOBet+J5eia6uriQx6//HNrcav//97/P000+bw/9jLvqPQgwABrB6vZ5arZYpU6ZUHWXAuP/++/PNb36z6hgDRq1WS71eX6mvYQ6f79xzz825555bdYwBY1XM4apg1l/YNttsU3WEAcM2tzrm8P/XKtvcqinEAGAAazQaaTab6e7urjoKA1S9Xk+j0Vipr2EOeSmrYg5XBbPOS7HNZSBolW1u1RRiADDANRoNH3qonDmkFGadgcAcwsrnovoAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRhlQdAAa6rq6udHd3v+Lnb7zxxuno6Mj8+fP7MVXZhg0bli233HKF12m9Xk+j0einVAAAAKwuFGLwIrq6utLZ2Zmenp4VXtbZZ5/dD4n4a9/73vdW6Pm1Wi3NZlMpBgAAUBiFGLyI7u7u9PT0ZNasWens7Kw6Dv2o2WxmypQp6e7uVogBAAAURiEGy6GzszOTJk2qOgYAAADQD1xUHwAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMSgYFOnTs306dOrjgEAAACrlEIMAAAAgKIoxIAkyTnnnJPx48envb096623Xg4++OCqIwEAAMBKMaTqAED15s2bl+OOOy4XXXRR3vSmN+Whhx7Kj3/846pjAQAAwEqhEIOCzZw5M0kye/bsrLnmmpk8eXI6Ojqy8cYbZ+utt642HLSQZrNZdQSAAa0/t5O2uQAvznbyWQoxIG9961uz8cYb5zWveU322muv7LXXXjnggANSq9WqjgartXq9nlqtlilTplQdhZXsNcMnZuP2zfPDhy+uOkrL6NioI53veF3u+OqdefKRJ6uOwypQq9VSr9df8fNtcxmIthmxdXYZtWM+/7svVx0F+ljRbW4rUIgB6ejoyPz58zN37txcc801OfnkkzN9+vTceuutGTVqVNXxYLXVaDTSbDbT3d1ddRRWsl9c2p1ff//hnPHfx1cdpWX89pmu/OeT385pB5+WUYPWrjoOq0C9Xk+j0XjFz7fNZSBacu3/Zsnsx3PEd99ddRToY0W3ua1AIQYkSYYMGZI99tgje+yxR/793/89o0aNyvXXX58DDzyw6miwWms0GsV/2CjBwz/5eRYOfSyTJk2qOkrLaH+kPfl5svnmr896w9erOg6rCdtcBprf/+o3+c2ge+wfYABSiAG58sorc//992fnnXfO2muvnTlz5mTp0qXZbLPNqo4GAAAA/U4hBmTUqFGZPXt2pk+fnieeeCLjx4/PJZdcks0337zqaAAAANDvFGJAdtppp8ydO7fqGAAAALBKDKo6AAAAAACsSgoxAAAAAIqiEAMAAACgKAoxAAAAAIqiEAMAAACgKAoxAAAAAIqiEAMAAACgKAoxAAAAAIqiEAMAAACgKEOqDgAkXV1d6e7urjrGgFKv19NoNKqOAQAAQAtSiEHFurq60tnZmZ6enqqjDCi1Wi3NZlMpBgAAQL9TiEHFuru709PTk1mzZqWzs7OyHIsWLcq+++6bGTNmZLvttqssR5I0m81MmTIl3d3dCjEAAAD6nUIMBojOzs5MmjSpstcfPXp0kmT8+PGV5gAAAICVzUX1AQAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAogypOgDQf7q6utLd3f2Knvvwww9nv/32yyOPPJL58+evUI56vZ5Go7FCywAAAICVRSEGLaKrqyudnZ3p6elZoeV897vfXeEstVotzWZTKQYAAMCApBCDFtHd3Z2enp7MmjUrnZ2dleVoNpuZMmVKuru7FWIAAAAMSAoxaDGdnZ2ZNGlS1TEAAABgwHJRfeAVeeqpp6qOAAAAAK+IQgwK8eSTT+a4447Luuuum/b29uy000659dZbkyQzZ87MqFGj+jz+8ssvT1tbW+/t6dOnZ+LEifna176WV7/61Wlvb1+V8QEAAKDfKMSgEB/+8Idz2WWX5cILL8z8+fPz2te+NnvuuWceeuih5V7Gvffem8suuyyzZ8/OggULVl5YAAAAWIlcQwwK8Pjjj2fGjBmZOXNm3va2tyVJzjvvvFx77bX5+te/njFjxizXcp566ql84xvfWO7HAwAAwEDkCDEowH333ZclS5Zkxx137L1v6NCh2W677dJsNpd7ORtvvLEyDAAAgNWeQgzIoEGDsmzZsj73LVmy5HmPW3PNNVdVJAAAAFhpFGJQgE022STDhg3LTTfd1HvfkiVLcuutt2bChAkZM2ZM/vKXv+Txxx/v/bprhAEAANCqXEMMCrDmmmvmmGOOyYc+9KGMHj06jUYjn/3sZ9PT05P3vve9WbZsWWq1Wv7t3/4txx13XG6++ebMnDmz6tgAAACwUjhCDApx+umn56CDDso73/nOTJo0Kffee29+8IMfZO21187o0aMza9aszJkzJ1tssUUuueSSTJ8+verIAAAAsFIoxKAQ7e3tOfvss/OnP/0pTzzxRG688ca84Q1v6P36/vvvn3vuuSc9PT353ve+l2nTpvW5rtj06dOdRgkAAEBLUIgBAAAAUBTXEIMW02w2i359AAAAeCkKMWgR9Xo9tVotU6ZMqTpKarVa6vV61TEAAADgBSnEoEU0Go00m810d3e/oucvWrQo++67b2bMmJHttttuhbLU6/U0Go0VWgYAAACsLAoxaCGNRuMVF1GjR49OkowfPz6TJk3qz1gAAAAwoLioPgAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUJQhVQcAntVsNit9/QcffDCbbLJJfve732X+/PmVZql6XQAAANDaFGJQsXq9nlqtlilTplQdJUkyderUqiMkSWq1Wur1etUxAAAAaEEKMahYo9FIs9lMd3d31VEGlHq9nkajUXUMAAAAWpBCDAaARqOh/AEAAIBVxEX1AQAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACiKQgwAAACAoijEAAAAACjKkKoDwOqg2WxWHYF+5t8UAACgXAoxeBH1ej21Wi1TpkypOgorQa1WS71erzoGAAAAq5hCDF5Eo9FIs9lMd3f3K17G0UcfndGjR+fUU0/tx2RlO+ecczJnzpxceeWVK7Scer2eRqPRT6kAAABYXSjE4CU0Go0VKk06OjoyevToTJo0qR9TlW3s2LEZNmyYdQoAAMAr4qL6AAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAURRiAAAAABRFIQYAAABAUYZUHQBWtmazWenrb7jhhuno6Mj8+fMrzdFKBg8enM0337zydVr1bAEAAPDKKMRoWfV6PbVaLVOmTKk6SpLkK1/5StURWs6cOXOqjpBarZZ6vV51DAAAAF4GhRgtq9FopNlspru7u+ootLB6vZ5Go1F1DAAAAF4GhRgtrdFoKCsAAACAPlxUHwAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiDKk6ALyYZrNZdQRYqcw4AADAqqcQY0Cq1+up1WqZMmVK1VFgpavVaqnX61XHAAAAKIZCjAGp0Wik2Wymu7u76igr7Oijj87o0aNz6qmnVh2lZZxzzjmZM2dOrrzyyqqj9It6vZ5Go1F1DAAAgGIoxBiwGo1GS5QEHR0dGT16dCZNmlR1lJYxduzYDBs2zDoFAADgFXFRfQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoCgKMQAAAACKohADAAAAoChDqg5A62o2m1VHGBA233zzrLnmmpk/f37VUVrGyJEjs9NOO1mn/8f3GgAAwMujEKPf1ev11Gq1TJkypeooA8rpp59edYSWc/HFF1cdYcCo1Wqp1+tVxwAAAFgtKMTod41GI81mM93d3VVHgWLU6/U0Go2qYwAAAKwWFGKsFI1Gww/nAAAAwIDkovoAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFGVI1QEYmJrNZtURgFXI9zwAAFAShRh91Ov11Gq1TJkypeoowCpWq9VSr9erjgEAALDSKcToo9FopNlspru7u+ooL2rOnDn5+Mc/nptuuint7e1Vx2kJixYtyr777psZM2Zku+22qzpOy9hzzz1z8MEHZ9q0aVVHeUn1ej2NRqPqGAAAACudQoznaTQaA/6H4udO79p6660zfPjwitO0htGjRydJxo8fn0mTJlWcpnUMHTo0G2ywgXUKAAAwgLioPgAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUBSFGAAAAABFUYgBAAAAUJQhVQeg/zWbzaojrHRtbW1517velTvvvDNDhw6tOk5LePTRR/Oud70rjz32WObPn191nJZxwAEHZMyYMUWs0xK2PQAAQGtQiLWQer2eWq2WKVOmVB1llbnwwgurjtByrFNWRK1WS71erzoGAADAi1KItZBGo5Fms5nu7u6qowCFqtfraTQaVccAAAB4UQqxFtNoNPwwCgAAAPAiXFQfAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIoxAAAAAAoikIMAAAAgKIMqToAfTWbzaojrBaeeOKJ/OUvf0m9Xk9bW1vVcVrC008/nYcffjhrrbVWhg0bVnWclvHnP/857e3tWXPNNauOslqwDQQAAFYFhdgAUa/XU6vVMmXKlKqjAFSqVqulXq9XHQMAAGhhCrEBotFopNlspru7u+ooq4U5c+bk4x//eG666aa0t7dXHaclLFq0KPvuu29mzJiR7bbbruo4LWPPPffMwQcfnGnTplUdZbVRr9fTaDSqjgEAALQwhdgA0mg0/BC4nJ47rWrrrbfO8OHDK07TGkaPHp0kGT9+fCZNmlRxmtYxdOjQbLDBBtYpAADAAOKi+gAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAURSEGAAAAQFEUYgAAAAAUZUjVAVpJs9msOkIxFi5cmCS5/fbb097eXnGa1rBo0aIkyT333JO111674jStY8mSJVm0aFHmz59fdZSi2B4DAAAvRiHWD+r1emq1WqZMmVJ1lOLsuOOOVUdoOcccc0zVEVrOueeem3PPPbfqGMWp1Wqp1+tVxwAAAAYghVg/aDQaaTab6e7urjoKAP+nXq+n0WhUHQMAABiAFGL9pNFo+MELAAAAYDXgovoAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRhlQdYKBoNptVR+BleLJnUB5/eGjWHvtk2tS6/eKpZ57JfY8+lI1GrJURQ4dVHadlLLn39xk0akQG19eqOgovk/0CAAC0ruILsXq9nlqtlilTplQdhZehc/1Ds8+WX8sX/3vdPL30iarjtIaRI5JjDk++9f3kN4uqTtMyLhqyTeYs/UMuWfr7qqPwCtRqtdTr9apjAAAA/az4QqzRaKTZbKa7u7vqKLwMD9zekZ/+Z3LTT36SIUOXVR2nJSx6/NHs+/1vZsaMGdluvQ2rjtMy/jT533L0gQfkxPe8reoovAL1ej2NRqPqGAAAQD8rvhBLni3F/MCzmnlkSX6aJ7L1xIkZukZb1WlawuiHupPvJ+PHj8+k13ZWHadlXD90WDYYu0FeO2lS1VEAAAD4P66+BAAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFEUhBgAAAEBRFGIAAAAAFKVt2bJly6oOAS/Xw39cmq67n8kWOw7JoMFtVcdpCY8/9WTm/Oqu7PzqTbNex8iq47SMP155Y2qveVU6Jry66ijASvTHe/8n3Qv/ks3fumHVUVrG/zz1P/nVo81stfbEtA9urzoOwCvyv795PI/f+1jqu69XdRTgbyjEAAAAACiKUyYBAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKIpCDAAAAICiKMQAAAAAKMr/BxF55yw0taUIAAAAAElFTkSuQmCC"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 22, 
+ The blamed thing is water logged he uttered disgustedly . The hands point to less than half past nine . Darrin managed to get at his own watch .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 23, 
+ I 'm growing terribly hungry . Catch a fish smiled back Darrin . And eat it raw gasped Dalzell .
+Skipping line 24 due to an error: Bobcat failed to parse "I ' m growing terribly hungry .".
+ sample -&gt; 24, 
+ What then is your slang for a pretty girl . Oh we call her a queen . And a girl who is who is n't pretty .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 25, 
+ And for being a bit worse than touge . Which did they call you demanded Belle . Dave started then sat up straight staring at Miss Meade .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 26, 
+ So I 'm a gold brick am I frowned Belle . You a gold brick stammered Dave . Why you oh go look in the glass .
+Skipping line 27 due to an error: Bobcat failed to parse "So I ' m a gold brick am I frowned Belle .".
+ sample -&gt; 27, 
+ Yet something in her eyes gave the midshipman hope . Belle he continued eagerly do n't trifle with me . Tell me will you marry me some day .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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VTXKNWH9nyalutCJqa9PtouNYQkx0iOJGMin8h8tE3SPff9udWdYS05jqNF9z6k6qOvliXJssKJuGUputzUZluSxyV5XVKSS0pyR6Yuyeuy9sw3a29a7psied1WJ19VAAAAAAAOHoEY0MG2VBoVfmlUUiltrZR21LTdz7aah0wtgie3lOYNh1IuKza0spsHWJFp/e7tB1RbfcV2HTuoRbDWbL5pGjKx4V1ldbMgr1mA55i2j5OVYjTALw3wSxMHWurvJyADAAAAAHQfBGJAB2p0jJ772OiTHVJuhjSmvzTAbyk7VUp2Nwu/3E1BV8cERUMGZmtn2db998vJ1pRhHfOktDEmHJw1hu9mqw9JtY3StqpwKLilUnpzs1RWbTR9rOS2CcUAAAAAAN0DgRjQgWobpQ+2Sd8fK03I6bpX9F144YWaN2/ePh+btG1bF110UYcd07IsuS3JnST5mrUfliGF712TCr5wtOI/0rkhyc0bCwEAAAAA3QR/ogKdwOPq2ruhfvjDHyojI0Mul6vN9S6XS5mZmfrBD37QpXV19XUAAAAAAOBAEIgBvUBWVpZWr16tjIwM2Xbsj7Vt28rIyNALL7ygrKysOFUIAAAAAED3QSAG9BITJkxQUVGRFi1apDFjxqh///4aM2aMFi1apKKiIk2YMCHeJQIAAAAA0C3wDjGgF8nKytKcOXM0Z86ceJcCAAAAAEC3xR1iAAAAAAAASCgEYgAAAAAAAEgoBGIAAAAAAABIKARiAAAAAAAASCgEYgAAAAAAAEgoBGIAAAAAAABIKARiAAAAAAAASCgEYgAAAAAAAEgoBGIAAAAAAABIKARiADpdyDHxLqFbaKipjHcJAAAAAABJ7ngXAKD3OjLLqJ9P+s2rUk6aowGp0oA0S1kpktclJbmkJHd42rTstiXLsuJd+gFzQiE11lWpsa5aDbVVaqxtmq9UXUW5yjcHVf75epUXf6iKrZ/pqzN/G++SAQAAACDhEYgB6DT+JFvfH2f0cZm0pdLo891SQYlRo7P3bSwZJdmO3FZIHoXkVoPcpl4up14up06WQrKMkeRIxshqmhpHkpFlQpIxkgnJkpGMI8s4yih7R6GKLdFlNfvELEe2ab7OhOqkhmqZuiqZhmo59VVy6qoUqquW01C7z2vgyxqozCF5OuzYbypzyCgdeeK5HXmJAQAAAAAHgUAMQKfKSalXilWkIeUfqrz4Q+3YvF7lu6tUHzKqC1lqcCw1GJcct0/GkyrH7ZMTmRp3qkKeVDVE2kySX7JckmU3m4Y/JtrmlrGsmPXGsmRt/q/Mtg+b9bVl1LSNFdNmLFtqmlqWHDtJIXe2nBS/nHSfTLTGPfUat0/ypMrlTZU72Se316fk1D7y901Xit9Shl8akCa5k3rO3W8AAAAA0FsRiAHoNMZx9OQv/1c7Nv1HkpTad7Ayc/OUO3SkPMl+uZNT5UlOldvriyz7Isup4XVeX7SPKynl0B6lPO9Ph3QuIceoPiTVh6S6pmmjom31MW3hvlUNUkmF9O8So4bIXXEXjZfG9CcUAwAAAIB4IhAD0Gk2Fz6nim2bNHneo8rJO0FJqX3iXdJBc9mWUmwpxXMgvWMDL8cY7aiRnvnI6NkiQyAGAAAAAHHGKJMAOk2osV4NNRUacNRxPToMO1S2ZSngszSmv6Vt1VJpFaNuAgAAAEA8EYgBQBcZ01/yeaQ3NhOIAQAAAEA8EYgBQBfxuCydmCu9ukn6cBuhGAAAAADEC4EYAHShU4daGp0t/WWdUVEZoRgAAAAAxAOBGAB0Iduy9P2xlg7PlP74ntFTHzlqdAjGAAAAAKArMcokAHQxj8vSjyZKr2yUnv3YKLjd6JQjpXEDJLfNCJQAAAAA0NkIxAAgDmzL0qQjpBEB6Z9FRis+MHqmSDoxVzp+sORLIhgDAAAAgM5CIAYAcTQwzdIlEy1tqTR6dZPRCxuMVn8qHT3QaEKOpcP6SC7uGgMAAACADkUgBgDdwAC/pW+PtnTaMKM3N0tvbDZ6c7OR1yUdmWU0oq+l4X2l/qmSZRGQAQAAAMChIBADgG4kzWvpG8OkyUOlz3dLH5dJRWVGT39kFDJSulca3ndPQJbuJRwDAAAAgPYiEAOAbsi2LOX2kXL7SKccaak+ZPRZeTgc+7hMKvgyPDLlAL/RYX2kbL+l7FSpv1/KSA5vDwAAAABoG4EYAPQASS5LIwPSyEA46KqoM/pkh/RxmdHnFVJhiVGDE+7rcUnZqUb9U6XsVEv9/VJ2qhTw8T4yAAAAAJAIxACgR0rzWpqQI03ICQdcjjHaWSttrZS2VUlbK422VUnB7UY1jeFtbEsK+MJBWb9UqU+ypXSvlOYNP4qZliR5XARmAAAAAHo/AjEA6AVsy1JWipSVIuX1k6RwsGWMUWW9tLWqKSwz2lYpFZZIu+uMHBO7nxS3UVqzkCwcmFlKT4ptS3bzcn8AAAAAPReBGNCB3LY0oq/06iajHdXSgDRpgD98500ihAfGGNXs3Kry4g9VvjmoTe8+o8HjJ8vl8ca7tIRlWVY04BqWJTUFZVL4rrKaBml3nVRRF56G540q6qVdtdLmXeHlulDsft225E8ySnFL3sgn2S0lu5ovW0p2S17XnvUt+/EIJwAAAIB4IBADOlCy29L4AdLrm43++bFRo9PULnndRkmucDiQ1OLjdUemLiva5rEduZ16SSFZxpElIxlHlhyFb+sJz1vGkUx4WcaEl+XIDtVLTp0sx5Ei2zb/NPWT4zTbb1Nbo0xDtUK1VWqsq1ZDbZUam8/XVUXaqvfM11Wrvnq3GmoqJEmupBRlDh6pESfPkNvri8eXA/thW5ZSk6TUJCknrfma1iFVXaNRRZ1UUb8nOKusN6prlGobpbpGqbpB2lETng+3tw7SWnLbJhyQRYIzt0tyWeHAzWXvmbdbtLnsyLJlhdtsyW3Fro9pi7Rb1p6zsxRebj5vRRYstZiP9LMPcL3Lal1/IoTiAAAAQE9BIAZ0sGMHWzp2sKWQ4+iLku36dHOJtu6sVV2No7pGqcGxVW9sVRmXGo1bDZZHIcujRjtZIdsrx5Uix5Oq8J/ZB39n1WHvzpNv3dJDPh/HnSqTlCp5fFJSqiyPT3aSX7bXJ1dShlz+VLm8qfJ4U+VL8Ssw6EgdNnyUBgw+TC4X/8T0Fl63Ja9bCqQ2b91/wOMYo/qQosFZU3hWF4pdbgrPGh0p5ESmJjyta5QaTTivbTTh9aHovAkvN7UbtXoMtLtwWSYmJGsZ7kVDv30Eek19va5md+C1ceddU7ubO/AAAACANvHXKtDBNrz6fwo+d7/KNwdVX71LkmS7k+RJ8SvNmypPcqrcXl942ny+aV2yTy4rVZY3XcabLst2S7ZLxrIluSTbJVl25HaW8Hx4nS3Z4amxbFnfmiGdOV2SJRNpb1qnyMeRHdl+z74c2TKWSyF3qhrsFDU4luoaw6FG06cpzGi+3BR6hEKS1kspn0gD/I5OzLU0Poc/yhOVbVnRO8D67LNnx32POKZ1SNYUtEnhGyqNIh8T27bX9W3Mm8hCU9/ofCSUa37clvPh0M/E1Ng8CGwK/eodKdS4JwB0HKnBCf/M1TWG79DbV/7ntk04IGv52Gqz5WR3+E7B6AALkffF8TgrAAAAejMCMaADNdRU6pNX/yYjo7HTfqrMIaOUOSRP/n65smw73uUdggP7w9gxRuU10pbK8Gf9dqN3vjAamCZl+/njGl3DtizZLskT70L269B/Jhxj1BBqdqfdXu/Gi328dXe9VFfd1M+oqr51sJbqMXtGII2MQprubTEyqTccqAEAAAA9DYEY0IEa62v1+drnNfnny3XYMd+MdzldzrYs9fVJfX3S6Gzp+CHSXW8ZLS0wOuVIaeJAKcnFH89AR7EtK/rI5L7t++fOMeFQbM/ACk3vizPaXSeVVUsby8PLDU7stkkuo3Rv+Oe+f6rU328pOzU870vi5x0AAADdE4EYgE7j81iadbT01HqjJz40+meRlD/Q6IgMS7kZUmYyLxoHugO72Wikg2LWxP58GmNU27hncIU9o5Maba+S/rstPMpu091maUlG2alStl/qn2pFpuG7y/jZBwAAQDwRiAHoVFkpli6aYKms2uj1YqMPtkmvF4f/XPYnSbl9jHL7WMrtIw1ICz+WxR/KQPdkWZZSPFKKR8reywALDSGj7dXStkppa5XR1krps3Lpnc/D70yTwu8uy04NP049rK+l4VlSKneTAQAAoAsRiAHoEn19lr51lKVvHSVV1BkV75KKd4WnazaG7zqRwiPoZSQbZSZLmSlSRrIVmSo6ZeQ8oPvyuCwNTJMGpknNg7KQY7SjRtpWJW2NhGWflktvfW5kSRqYbjSirzS8r6UjMsL7AQAAADoLgRiALpfmtTQ6WxqdHf6D1zFGpVXStmppZ41UXht+OX9JpRTcblRRv2dbS5LPY8J3qbjDd6okR6bhZWsv7eEpf2QD8eGyLfVLlfqlht8x2BSW7aw1+rhM+rjM6L0vpJc+M3Lb0pGZRsP7WhrRV8pJCz/WCQAAAHQUAjEAcWdb4XcLZfubWmL/8G0IGe2slXbWSuU14XcW1TYa1TRKNQ3hkfLKaxRZDrc7LYfMi3DbRiluKbkpKGsWnnlckssK36Xmsq1m85Gp1XreZUvuZv1ctmK2az7PH/RAaxnJlo4ZJB0zyJIxRlsqpaIyqajM6PlPjJ4pCj9KffyQ8CfNy88RAAAADh2BGIBuz+Pac2fJHnv/o9gYo/pQOChrCs2i04amdhNtr6qXtldLjY4Ucpqm4fcdRZf3ErC1h22ZVkFae+ZdVvism3K16DQy33RFDqhPi/lwH2v/fZpq2Od+9nKsZm22FXt+bQWILQPI5u28Z653sixLOWnhO8JOOtxSo2O0cae0bovRS58Z/eszaWKO0dcOtzTAz/cAAAAADh6BGIBex7Ised2S1y312Xuvdu3TGCPH7AnHmsKzkCM1mj3zMeuazTfvE9vfxAZxLUK4Bicc4DVtYyTJhKcx85HArlV7u/qYvfdp3t7OPp3BZZl9B2j7CRbbvvPPigksva7Wj9wmu6UkF4FcV3HbloZlScOyLJ02zOitz8ODcrz9hdHIgNHpwy0NTudrAQAAgPYjEAOAA2BZe8KSDt5zR++wWzLGxARljmkd/rU5f6CBYxt39LUMIOsa2wg0TfM+Jqb/3h67tS0pxb3v99gluyUf77HrUKlJlk45UjrpcGndFulfnxnd9ZbRlGHSyUfwSDIAAADah0AMANDpLMuKPjYpSS5Jno49QofuTQq/u66tR25rGiOP3TbErt9RE9u+t0At1WOUmSJlJksZKVJmZCTVpuVUD3eg7YvbtjRxoDRugPT8J0arPjZaXypdnC8lu7luAAAAODAEYgAAtMHjsuRxSenevfXY/3vsmg/8UNMgVTWEB4fYWWNUXiutLw3PNzjNjmtLmSlGg9Kk3AxLuX2kgWncWdaS27Z0xghLIwNGDxQaPfxvo4vzwwNiAAAAAPtDIAYAQAdr/h67jOQ2e0TnjDHhoKxGKo+MpLqjxmjzbumDovA75lyWNDDNaEgf6YhMS1/JJiBrMjTL0swJ0v0FRk9/ZDQ1j+sCAACA/SMQAwAgjizLkj9J8idJg6OjQIRDnUbHqKRCKt4lFe8y+rhMemOzUWqSdPxgoxNyLaV7CYCG9w2/X+ylz4xOHWbk83BNAAAAsG8d/npoAADQMdy2pSF9LJ2Ya2n6GFvzvmZr3v9YmjBAemWTtOg1oy92d9ZYnu03c+ZMTZs2Lbo8adIkXXXVVV1y7OOHhN/bVvBllxwOAAAAPRx3iAFAL1VcXKzS0tJ4l4FOkCupf4ZLL+4cpkffa9DJGZ8e8j7nz5+vp59+Orrcp08fjRo1Sj/96U81fPjwA9pHWVmZKioqVFhYKEmqrKzUtm3bosudLcM1XOs3VSu17IsuOR7iJxAIKDc3N95lAACAHoxADAB6oeLiYuXl5am6ujrepaATuTxehRrqOmXfu3bt0ptvvqk333yz3dtOnDgxOl9QUKAVK1Z0ZGl7Z1mS6T53zKHz+Hw+BYNBQjEAAHDQCMQAoBcqLS1VdXW1li1bpry8vHiXgx5g/vz5qqio0G233RZtW7t2rS655BKtXr1amZmZ2rJli26//Xa99dZbsm1bEyZM0Ny5czVw4MA29zFr1iyNGDFCc+fOlSTV19fr3nvv1XPPPaeKigoNHTpUV155pY4++uiuP2H0WMFgUDNmzFBpaSmBGAAAOGgEYgDQi+Xl5Sk/Pz/eZaAH6Nu3r1wuV/T7pbKyUkuXLtWwYcN08sknKxQKacaMGTr++ON12223ye1266abbtLcuXP1/vvvKykpqdU+/H6/srOzo8s/+tGP9Omnn+rxxx/XwIEDtXLlSl155ZX64IMPDvixTAAAAKAjEIgBAABJ0tNPPy2/3y9JqqqqUk5Ojp5++mnZtq3ly5fLcRzdf//9sqzwKI4PPvigMjIytGbNGn3jG9/Y576Li4v14IMPqri4OHpH2dy5c7Vq1So9+OCDWrhwYeeeHAAAANAMgRgAAJAknXzyyfr9738vSSovL9d9992n008/Xe+8847WrVunTz75RGlpaTHb1NbWasOGDfvd9wcffKBQKKQRI0bEtNfV1alv374ddxIAAADAASAQAwAAkqTU1FQNGzYsunz//ferT58++uMf/6jKykpNnDhRjzzySKvt+vXrt999V1ZWyuVyqaCgQC6XK2Zd011pAAAAQFchEAMAAG2yLEu2baumpkb5+fn661//quzsbKWnp7d7XxMmTFAoFNK2bdv0ta99rROqBQAAAA4cgRgAIEZxcbFKS0vjXQYOQHFxsbZs2aJjjz32kPdVVlamHTt26Pnnn5ckVVRU6K9//asqKys1cuRIjRo1Smlpafrf//1f/fjHP1Z2drZKSkr0r3/9SxdddJH69++vsrIyVVRUqLCwUFL4rrBt27ZFl08//XR973vf09VXX62RI0eqvLxc77zzjoYPH94hIdl//vMfeTwejRw58pD3ha4RCAQYKRIAAMQFgRgAIKq4uFh5eXmqrq6OdymIkylTprRqu/TSS6PzxcXFuuSSS2LW//Wvf41ZnjhxYnS+oKBAK1asiFn/s5/9rCNKRS/g8/kUDAYJxQAAQJcjEAMARJWWlqq6ulrLli1TXl5evMvBfixdulRPPPGEVq1aFe9SuoXZs2crKyuLESt7iGAwqBkzZqi0tJRADAAAdDkCMQBAK3l5ecrPz493GdiPnJwceTwevlYRaWlpyszM5HoAAABgv+x4FwAAAHqehx56SBkZGfEuQ5K0YMECjR8/Pt5lAAAAoAchEAMAAPt0+OGH64477ohp++53v6uioqL4FAQAAAAcIh6ZBAAcMkamjI/+/fvr7LPPjo7i2Fnq6+v1+eeft3mczz//vFOPfSBKSkpUU1OjE044QSkpKZ1+PdA2RowEAAA9CYEYAOCQMDJl/N17772dfoxbb71Vt956a6cf51DcdNNNkqRrr702zpUkJkaMBAAAPQmBGADgkDAyZfzdddddWrlypa655hqNHz9epaWl2rhxo0477TRNmzZNY8eO1aWXXqodO3bopptu0oQJE3T99ddLkubPn6+XXnpJp512mqZPn67Nmzfrl7/8pebMmaNzzjlHu3bt0vTp03X22Wfr7LPPlhS+E+jJJ5/UrbfeqpdfflmStGTJEi1btkzHHXecLr30UlVUVGjevHmaOnWqfvKTn0iSzjzzTJ1//vk6//zzo7VPnz5dkyZN0qWXXipJuuyyy+T1evWjH/1Ifr9fjz/+uJ566imtXLlSffr00fPPP6/58+dr3rx5Gj9+vP75z3/q0Ucf1aBBg7RixYquvOxohhEjAQBAT0MgBgDoEIxMGR8VFRV69NFHdc899+iSSy6JWffHP/5RoVBITz75pFJTUyVJubm5Ouuss3T//ferf//+6tu3rwKBgP7617/K5XJJkt544w198skn0a9ncnKyRo4cqW984xvRfb///vtyuVzRPjk5OZKkv//970pLS5MkffTRR3rllVeifZKSkjR48OCY75OUlBTl5OQoPz9fr732moLBoLZt2yav1ytJmjZtmoYNG6aPP/5Ys2bN0uWXX65LLrlEN9xwgyTpnHPO0X/+8x/V1tby/QcAAIADxkv1AQDowYLBoOrq6nTKKae0uW7cuHHRMEySTjzxRDmOo48++ijaNnr06GgYJoXDrW3btrW7lsMPPzwahh3MftatW6fKykr17dtXfr8/+vnss8+0YcOG6Dl99atfjdnu+OOPb3etAAAASGzcIQYAQA+WkpJyyPvweDwxy5ZlyXGcDt+PbdsyxsT0aWhoiM5XVlYqJydHa9asabXvjIyMdtcDAAAA7A13iAEA0IMNHz5cKSkpevHFF1uty8vL07p161RVVRVte/3112XbtkaOHHnAx0hKSlIoFDrkWvv166eSkpLo8u7du/XZZ59Fl/Pz87Vlyxa53W4NGzYs5hMIBKLn9Pbbb8fs96233jrk2gAAAJBYuEMMABA3xcXFKi0tjXcZPd6FF16oOXPm6Msvv9S4ceO0c+dObdiwQVOmTJHL5dLUqVM1a9YslZeX68Ybb9QZZ5yhL774Ql988YXKyspUUVGhwsLC6P62bdumysrKaFtWVpaeeuopjR49Wh6PR5mZmdq4caNCoVC0T0lJiWpqamL28/nnn6u+vj7aNmrUKD3wwAMaOXKk/H6//vCHP8iyLJWUlKiwsFBZWVkaM2aMpkyZoiuvvFKHHXaYtm/frtdee00nn3yyRo0apW9961tasGCB+vXrp3HjxunZZ5/VBx98oEGDBsUcGwcvEAjwYnwAANDrEYgBAOKiuLhYeXl5qq6ujncpvcb8+fNjlm+88UZJ0osvvhhzB9kXX3yhp59+OqbvxIkTW+2vZVvTiJL76nMg+2n58v+lS5dq6dKlMW2XX355zPIDDzwQs9w0SmaToqKiNo+N9vP5fAoGg4RiAACgVyMQAwDERWlpqaqrq7Vs2TLl5eXFu5weaenSpXriiSe0atWqeJfSLcyePVtZWVlauHBhvEvpsYLBoGbMmKHS0lICMQAA0KsRiAEA4iovL0/5+fnxLqNHysnJkcfj4fpFpKWlKTMzk+sBAACA/eKl+gAAAAAAAEgoBGIAACCuFixYoPHjx8e7DAAAACQQHpkEAPQojEy5R0lJiRoaGnr86IptjVB5MCoqKuTxeHr89ehIjBgJAADQNgIxAECPwciUbestoyt21Hk899xzHbKf3oARIwEAANpGIAYA6DEYmfLgOY6jhx56SCtXrlRZWZlyc3N1ySWXaPLkyQqFQrr55pv17rvvqqysTAMGDNB5552n888/P7p9Y2OjbrvtNj3zzDNyuVyaNm2aSktLVVlZqdtuu22/x5Ck9957T5deeqnuuusu3Xffffrkk0907733qqCgQGvWrNGKFStUWFio2bNn65///KcCgUD0+IsXL1YwGNSf/vSnrr1wPRgjRgIAAOwdgRgAoMdhZMr2u/nmm/Xiiy/qgQce0PDhw/XKK69o9uzZ+upXv6oTTjhB48aN03XXXae+ffvqjTfe0KxZs3TsscfqO9/5TnT7F154QQ8//LDy8vJ055136tVXX9XJJ58c/Vrs6xgnnXSSdu/eLUm6//77ddddd+nII49UZmamvvzyS6WkpCg/P1/5+fm65ZZbtG7dOv3sZz+TJDU0NOiFF17QokWL+LoDAACgQxCIAQDQy9XV1WnhwoVavXq1jj/+eEnSkUceqddee01LlizRSSedpOuvvz7a/4gjjtCbb76pv/3tb9FA7O6779Yvf/lLnX322ZKke+65R//85z/bdYwmN9xwg0499dS91nvxxRfrwQcfjAZiTz31lGpra6O1AAAAAIeKQAwAgF7uk08+UXV1dasQqr6+XhMmTJAk3XvvvXrggQdUXFysmpoa1dfXR0d+3LVrl7Zu3apjjz02uq3L5dLEiRPlOM4BH6PJ0Ucfvc96Z86cqeuuu05vvfWWjjvuOD300EP6zne+o9TU1IM6fwAAAKAlAjEAAHq5yspKSdIzzzyjQYMGxazzer169NFHNXfuXN166606/vjjlZaWpltuuUVvv/12hx2juf0FW9nZ2TrrrLP04IMP6ogjjtCzzz6rNWvWHHAtAAAAwP4QiAEA0MuNGjVKXq9XxcXFMY8uNnn99dd1wgkn6LLLLou2bdiwITrfp08f9e/fX++++66+/vWvS5JCoZAKCwujd5Ht7xjtdckll2j69OkaPHiwhg4dqhNPPPGQ9wkAAAA0IRADACSE4uJilZaWxruMDlVeXq7KykoNGTJkv31nzJihK664Qp9++qnGjx+vyspKrVu3TqmpqUpOTtbbb7+te+65R4MGDdIzzzyjt956S4MGDVJhYaEk6dxzz9WNN94oy7J0+OGH669//atKS0u1a9euaJ99HeOss85SUVGRJGndunVKS0uL1lZSUqKamprofiSpX79+Sk5O1o033qjZs2fHrNubkpISuVwuZWdnt+s69gSBQICRIgEAADoQgRgAoNcrLi5WXl6eqqur411K3C1YsGCv66644oqY5aKiIk2cODGmbc6cOTHLa9asadWnrWM0b5s0aVKbx2+5nyZ333237r777r1UnRh8Pp+CwSChGAAAQAchEAMA9HqlpaWqrq7WsmXLlJeXF+9yOszSpUv1xBNPaNWqVV1+bMdxdN5552ny5Mkxj1p2pBtuuEHl5eW6/fbbD6j/7NmzlZWVpYULF3ZKPfESDAY1Y8YMlZaWEogBAAB0EAIxAEDCyMvLU35+frzL6DA5OTnyeDxdck6bNm3S888/r5NOOkl1dXW655579OWXX2rOnDkdHjLu2rVLH3zwgZ577jk9+eSTB3x+aWlpyszM7FVfYwAAAHQOAjEAALBftm3roYce0ty5c2WM0Ve+8hWtXr26U+64mzp1qt555x3Nnj1bp556aofvHwAAACAQAwAA+zVkyBC9/vrrXXKsNWvWdMlxAAAAkLgIxAAA2IfuPDplSkqKjj/++AMagbGznXnmmVqwYIGOPvpoffnllzrrrLO0fPlyjRw5sstqGDFihFJTU7vF9dgXRowEAACIPwIxAAD2oqeMTvn444/HuwRJ0qWXXhqzfP7558eljltvvTUuxz1QjBgJAAAQfwRiAADsRW8dnfJgzJo1S8OGDZPX69Xf//53eTwenXvuudEQrPkdYhMnTozZduLEiVq6dGk8yu52GDESAACgeyAQAwBgP3rb6JQHw+/369lnn9U111yj9957T2+++aZmzpyp8847T6eeeqqSkpI0YsQI5efn65133tGxxx6r1atXa/To0UpKSlJWVla8TwEAAACIIhADAAAHZOzYsZo/f74kafjw4brnnnv04osv6tRTT9XGjRuj/fr16ydJ6tu3rwYMGBCPUgEAAIB9suNdAAAA6BnGjh0bs5yTk6Nt27bFqRoAAADg4BGIAQCAA+LxeGKWLcuS4zhxqgYAAAA4eDwyCQBABysuLlZpaWm8y+hQlZWV2rZtmwoLC6NtO3fuVCgUimmTpO3bt0uS/vvf//bqwCwQCPBifAAAgB6KQAwAgA5UXFysvLw8VVdXx7uUDldQUKAVK1a0an/66afb7D9jxozOLimufD6fgsEgoRgAAEAPRCAGAEAHKi0tVXV1tZYtW6a8vLxOPdbSpUv1xBNPaNWqVZ16HEmaNWuWRowYoblz50bbrrnmGqWlpen6669v1X/lypX64x//qO3bt2vChAlaunRpp9c4e/ZsZWVlaeHChZ1+rGAwqBkzZqi0tJRADAAAoAciEAMAoBPk5eUpPz+/U4+Rk5Mjj8fT6ceRpPfee69V25o1a/baPz8/XzfeeGMnVtRaWlqaMjMzu+R6AAAAoGfjpfoAAAAAAABIKARiAACgXRYsWKDx48e3auvfv78sy9Lf//53zZw5U9OmTYtLfQAAAMD+8MgkAADdRHtHpywpKVFDQ0OrUR4PVlvvCWvL5MmT9fWvfz163M8++0zXX3+9Fi9erDFjxig9PV1lZWWqqKjosNoOREVFhTwez0EdkxEjAQAAEguBGAAA3cChjE45ceLEDqtjbyNJHoi2grSOrO1APffcc+3ehhEjAQAAEguBGAAA3UBXjk7Zlvnz5+vpp59u1Xb99dfrrrvu0n333adPPvlE9957rwoKCrRmzRqtWLFCS5YsaTWCZEFBgebPn6+KigrddtttkiTHcfTQQw9p5cqVKisrU25uri655BJNnjy5y85xbxgxEgAAIPEQiAEA0I10xeiUbVm2bJlOP/10feUrX9ENN9wgSfrvf/8rSbr//vt111136cgjj1RmZqa+/PJLpaSkKD8/X7feequOP/54/eAHP1BJSYkkacCAAerbt69cLlf0XG6++Wa9+OKLeuCBBzR8+HC98sormj17tr761a/qpJNO6vLzBQAAQGIjEAMAAOrTp4+SkpLk8/k0YMAASdL69eslSTfccINOPfXUNrfz+/3KyMiQpOh2LdXV1WnhwoVavXq1jj/+eEnSkUceqddee01LliwhEAMAAECXIxADAAD7dPTRRx/S9p988omqq6tbhWr19fWaMGHCIe0bAAAAOBgEYgAAYJ9SU1MPafvKykpJ0jPPPKNBgwbFrPN6vYe0bwAAAOBgEIgBAABJUlJSkkKhUIfvd9SoUfJ6vSouLubxSAAAAHQLBGIAAPRSxcXFKi0tPeD+fr9fL730kp5++mmlpKTo448/liStW7dOaWlp0X4lJSWqqalRYWGhJEX7NS1LUllZmSoqKqJtM2bM0BVXXKFPP/1U48ePV2VlpdatW6fU1FSdddZZ7TqvQCDAaJAAAAA4JARiAAD0QsXFxcrLy1N1dXW7t20ZUE2aNKnNfhMnTtznclttCxYsaNWnrbZ98fl8CgaDhGIAAAA4aARiAAD0QqWlpaqurtayZcuUl5fXqce6/vrrtXHjRj344IOdehxJCgaDmjFjhkpLSwnEAAAAcNAIxAAA6MXy8vKUn5/fKft+6KGHdNVVV+mcc87R9u3b93ocy7K0cuVKTZs2rVPqAAAAANrLjncBAACgZ/rud7+roqKi6PKCBQs0fvz4+BUEAAAAHCDuEAMAAAclJSVFKSkp8S4DAAAAaDfuEAMAAFFPP/20+vTpo3fffVeFhYVasWKFLMvSzJkzVVhYqMLCQk2bNk1nnHGGFixYoLS0NHm9XtXW1ur666/XunXrZFmWLMvSggULoqNMFhQU6OSTT1ZycrJyc3N1++23q7CwUMXFxXE+YwAAACQi7hADAABRhx9+uHbv3q1jjz02pv3Pf/6z/vznP8e0Pfvss5KkP/zhD23u6/rrr9f1118vSbrpppui7Zs3b9Y111wjiREjAQAAEB/cIQYAAKLq6+slSdOnT1dBQYEmTZqkn/zkJ/J4PHr11VejIdjKlSs1f/58+f1+FRQUqKCgQLNmzdKIESOiy00fSbr44oujy6+99pok6Wc/+5mqq6tVWloan5MFAABAwuIOMQAA0MrmzZs1YcIEffDBB/r973+v1157TZWVldqxY4cGDhyoadOmaefOnXK5XNHRJXNycpSSktLmaJNTpkyJaU9PT5ff7++y8wEAAACaIxADAACtFBYW6tFHH5UkVVdXa9SoUXr00UdVUVGhMWPGqLCwUBs3blQoFNJf/vIXVVVVqaSkRDU1NdH3hjVXXFwc0+44jr744osuOx8AAACgOQIxAAAQFQgElJKSourqap1//vmSpIkTJ7bq99xzz0XnL7zwwph1bfWfO3duq7alS5fK5/MpEAgcatkAAABAu/AOMQAAEJWbm6v169dr5MiRcrlc+uUvf6mCggL961//ktsd/v/RHn/8cRUUFETfIfatb31LY8eO1U033aSUlBQtX75cq1ev1ptvvhl9h9jixYtj3ivm9/s1f/58XqgPAACAuOAOMQAAECM3N1ennXaa7rzzTl144YU66qijJEmjR4/W1q1bdc4550iS3n//fblcLvXt21fbt2/X3LlztXbtWl122WXauXOnHnzwQc2cOVOSNHTo0Jh3iLlcLh1++OGEYQAAAIgLAjEAANDKHXfcoTvuuCOm7d///nfM8syZMzVz5kz98Ic/lCR5vV499thjrfZljGnVtnPnzo4qFQAAAGg3AjEAAHqxYDDY6ccoKytTVVVVmy/T72hdcT4AAADo/QjEAADohQKBgHw+n2bMmNFlx2zrZfqdgRfxAwAA4FARiAEA0Avl5uYqGAyqtLQ03qV0uEAgwLvHAAAAcEgIxAAA6KVyc3MJjgAAAIA22PEuAAAAAAAAAOhKBGIAAAAAAABIKARiAAAAAAAASCgEYgAAAAAAAEgovFQfAIBuJBgMxruEhMM1BwAASDwEYgAAdAOBQEA+n08zZsyIdykJyefzKRAIxLsMAAAAdBECMQAAuoHc3FwFg0GVlpYe8DZLly7VE088oVWrVnViZT3H7NmzlZWVpYULF7Z720AgoNzc3E6oCgAAAN0RgRgAAN1Ebm5uu0KZnJwceTwe5efnd2JVPUdaWpoyMzO5HgAAANgvXqoPAAAAAACAhEIgBgAAAAAAgITCI5MAAHSCrhi5sKSkRA0NDSosLOz0Y/UEFRUV8ng8XXI9GJkSAACgZyMQAwCgA8VjtMiJEyd22bF6gueee65LjsPIlAAAAD0XgRgAAB3oYEaLRM/EyJQAAAA9F4EYAAAdrL2jRQIAAADoWrxUHwAAAAAAAAmFQAwAAAAAAAAJhUAMAAAAAAAACYVADAAAAAAAAAmFl+oDALAfwWAw3iW0afv27SovL9eIESPiXUq3sHHjRrndbg0ePDjepexVd/1eAgAASDQEYgAA7EUgEJDP59OMGTPiXQp6EZ/Pp0AgEO8yAAAAEhqBGAAAe5Gbm6tgMKjS0tJ4l9KmpUuX6oknntCqVaviX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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 28, 
+ I 'm not going to reach 2 . 5 in some studies this month groaned Dan . I know that much by way of advance information .
+Skipping line 29 due to an error: Bobcat failed to parse "I ' m not going to reach 2 .".
+ sample -&gt; 29, 
+ Here 's the very proposition . The others crowded about nodding . I remember that one laughed Freeman lightly .
+</pre>
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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 30, 
+ Why it does seem easy confessed Farley . It sounds foolish now grinned Darrin . I 'm beginning to feel ashamed of myself .
+Skipping line 31 due to an error: Bobcat failed to parse "I ' m beginning to feel ashamed of myself .".
+ sample -&gt; 31, 
+ Dave asked with his charming smile . Suppose we talk about yourselves . My but you girls are good to look at .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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cQAAAAAKBRBDAAAAIBCEcQAAAAAKBRBDAAAAIBCEcQAAAAAKBRBDAAAAIBC+f+PjqpZfpSL2QAAAABJRU5ErkJggg=="/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 32, 
+ Are you in any trouble here . No I 'm thankful to say . Belle I 'd rather not answer that .
+Skipping line 33 due to an error: Bobcat failed to parse "No I ' m thankful to say .".
+ sample -&gt; 33, 
+ You 'll present me wo n't you . Assuredly as soon as I come back . I have a little commission to attend to .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 34, 
+ I I really do n't want to dance with him . Dave could only bow which he did . Then other midshipmen were presented .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 35, 
+ Mr Darrin I do n't choose to believe you sir . Dave 's face went crimson then pale . Do you realize what you 're saying Mr Treadwell .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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odN/7hvv7r3dahjN5sSU6reP844yTB4/+DQgqVlKvfvUZl/t8r8uxXY8L12L/9SZjCklMFeq8sDAABotQjEgBg24DSH9u4Mad2nFVr2anmNxwxDSmij8Jf8FJvcSYZc8ZLdIdkdhuxOyeGUHA4jvMwZfszhNCrvw+s5nJHHqpZH1glvV7mOo7Itp6KPESjUX0UwqLJghcoq70srKqrmK8L3pcFylVVE1ousUzVfVhFUaXS6cptoO1XrlpSXa9f+gH7cVyR/oFj+fcWqCIVq1NPOnaihGcfo4n5DlBLvtuhZaTydjrHruBMcWr8qqHf/Xqry0pqPO1xSmxSj8v1jKN5d+Z6pfP84XLXfS3aHEX68+nunjvdU9L3mkByuut9LNhvvnfoyQyGFyitklpUrVFahUGmZQmWR+eq3astKw/dmWYVC5VXz0WWRdcrLa7Ub3BdQWbUALFgUqFGPYbcrqV8PtTttkDy/Ot6iZwUAAKD1M0zTNK0uAmjOytbeJCMuXc4ek60upVHt32eqeJep4j0h7dtjhm97zeh0YK+p8jIpWCFVlJsKVkjBcilYYaqiXKqokIKR5ZW3n6sqQAh/2bdVnuRtGJKM8H10+oDl4QlTITOkkEwFzZCCoVD43gwpaAaj05JkVvtPkkwjJPOA5aZp6vOuH2hXaE94PUPRe1WflynTqPxoNUwZNsmwS4bNrJq3he/DhYfbD5mmTFPRfZkK974yTSlkhqrmpRqhVVmwQqGf+VFut9nkstvlsjvksjsU53DUmHc57IqLTjvUNsGt9MQkedxt5Elso/TE8H14Okkd2iTHTKAZrDBVtDP8fineYyqwp2o68t7Zvy/yngnfV1S+fyqqv2fKw++lYIX0c38z22w1Q2qb/YD3S+WPpsb7x1b13jEMU2bIlEJBKRQK34JBmaGgFAzJDAalYLCy0FDlvSnDNMPT1ecVnu9krlWbb/PCbxeZMirfa4ZClfPh5dWnbUZINiP8frHZJFvl+8dmhLc3jMj+wu+NyL7MUFUdZuXjkWmzIlgjpDIrgj/vyZZkuJyyuZyyuRyyxblkczlkOA+Yr1zHnhAnV3pbudqlyJWeKme7VLk8KdFlcZ08sifE/eyaAAAA8NPoIQZAkpSQaCgh0VD60Q0ztGAoZCoU+eJ/wJf9aIhWGagFK5dXVE5Hl1cLDSrKa3zPDn+VNiUzJO0M7NOW3bv03d7d2la0V/vLy1VaXqHyYDjsMiq/XhsyZJiGbIZN8Q6n4uxOuex2GTJkky18bxiK/GeLPGJUzae62sheYcg0Dck0FPk+blTOh2+qvBkyQ0b4GMqkULCyErNqH1K4N4/TacjpMpSYbFNKO5tS29mU4jFksxlVdRlGZfBn1Bla1ZivnI5zOKutY6+2XjjkimxrtzGk5JGyOwyltjeU2r5h2jNNM5w/lVcLoCOhcx3vkYry2kF1rfdSUNH3S/S9Y0qh8grt37JNJd/7VbLVr/I9+8I9m0rLK8PXmim0KUO2OKeMuDjZXC7JXpmihVPecBIXnTdUmWbJNGxyOZwy2qZX5ldV7ZlmZTRmVs5HbiEpGDRUXi5VlBvRbSJpnqnwe8LuNORwGXIl2JTcLnxr29EuV3y11Lzy/SNDMpyOaHhVFWQ5ay+LC99H168MtmwuZ+U64bDLcNhjJvwFAABoTQjEADQKm82QzRU+dSyscb4w3vj6C3p42WIZhqG+Rx2lXxzdTb2SU9U2wa22CW6lJSSGp91uta2cdjtdP+MLbL8jrrWi3NT+YlP7i0wFihWeLjYVKKpcVmRq6/qQtiwJqrxUOspr0xV3uJWYzJftWGIYhux2yW6PLmmU/exZ4VP+xX+Rbcs2pbZLUeovj1ObAV3lbJssR9skuSrvnW2T5ExNrrxvI6OqsMP0S0kXHtGWoZCp0v3S/iKz9vum2NT+YmnnDyGt+iqoXdtMufZKo6+N1+AznUdYKwAAAFo7AjEALVZ5sEIrtm7WjSefqdvPHKXk+ASrS/pJDqehpLaGktr+9HrBClNrl1do8cIyFW4M6tgBfFSj4e38ZLUSunbUkFceUJu+xzTrXk42m6GExHBP1kPZsyOkN/9Wqi8/qSAQAwAAwEFxjgyAFss0pY83fquBnbo0+zDscNgdhnr0d+j7b0IK7GWYRzSOks0/qHzHXiX169Gsw7DDldLOpq697fItr9Aef+jQGwAAACAmEYgBAIBWZdDpTiUmG/rXQyXi2kEAAACoC4EYAABoVdxJhi66MV7rPgtq2avlVpcDAACAZohADAAAtDre4x066TynXn2yVAvu3a/9xfQUAwAAQBUCMQAA0Cqdd3WcfjM1Xr7lFZo5cZ++XlFhdUkAAABoJrh0GQAAaJUMw1DW6U4d09euF2aW6Jm/7FdXr00nn+9Sv5Mdsjtaz8UEAAAAcHgIxAAAQKuW2t6m39+ToDX/qdCyV8v1/D0lSm5naOi5Tg09x6mktnSYBwAAiDUEYgAAoNWz2Qz1O8mpfic5VbgxqGWvleuDF8r0/vNlOqafXX1+6ZD3lw55OhOOAQAAxAICMQAAEFM6dbfrouvtOufyOK3MK5dveYXenFOq154uVfrRNnl/GQ7Iuh1n57RKAACAVopADAAAxCR3sqGTfu3SSb92qXS/qW9WVsi3PKiVH1Too3+XKy5BOjrTrq697erS26auXjunVwIAALQSBGIAACDmxSUY6nuiU31PdCoUMrV1fUjfrKxQwVchffZeuZa8YEqS2nYw1NVrV0Yvu7r0tqtTd5viEuhFBgAA0NIQiAEAAFRjsxk6uqddR/e0S5JM09SeH01t/iqogq+CKvgqpC8/KVVFWXj9FI+h9KNtSu9iU/tq9ynphmw2wjIAAIDmiEAMAADgJxiGodT2hlLb2zTgVKckKVhhqnBjSNsKQvpxS0g/fhfSxi+C+nRRuSrKw9s54yRPZ5vad7Ep/eiq+/Sj6VUGAABgNQIxAACAw2R31OxFFhEKmtq13dSP34W0fUv49uN3Ia1fXa7iXWZ0vdR0Q57O4V5kyWk2JbczlJxmKCnNUEo7m5LSDDldhGYAAACNhUAMAACggdjshtp1MtSuk029f1Hzsf3FVUHZj1tC+vH7kPzfhbRhdVB7d5oKltdcP6GNlJwWDsfCgZlNyZXTSWlVQZornuAMAADgcBGIAQAANIGENoYyetuV0dte6zHTNLW/SNq7M6S9O0zt3WmqaGdIe3ea2rvD1K4fTG1aW66inabKS2tuG++Wkg7oZZbcrjI8SzPCj6UZinOHT/8EAAAAgRgAAIDlDMOQO1lyJ9vVsdvB1zNNUyUBae+OkIp2mtHAbO/O8PyeHaa2fB1U0U5TpftrbmuzSfGJUnyiofhEQwmV97WX1b1OQqIhh4tQDQAAtA4EYgDQjIVC5qFXAo6AGQpZXQKOgGEYSkiUEhLt6pDx0+uWBMzK0Cwclu0vNlWyz9T+fVLJPjN621ko7d8XqloWOHibdocqg7Kq0Cw+0VC8u3J5m4MvS0iU4t2G7E5CNQAAYD0CMQBohlxxUt+THfr3rFLlL65Q5x52tWlrqE2KocTILTl8b3fwxRK1BfeXquzHXSrbsUdl/t0q+3G3ynfsVsn3P2rPZz7tWfGVut1wqdVlohHFuw3Fuw2lH207rO1CoXDvspJiU/ujwZmqTYfnw+FaeL5oZ+WyQDh4Kys5ePuGITlcktMlOVxGjXunK9wLzRknOZxG+P7AxyrXd1Quc7okZ5whh1M11j9wO4dLstn4vAQAAGEEYgDQDDlchs641KWOXW0q+Cqo5YvKtW+PqVCw9rrxiZI7yZDDGe554XBIdqchuyPcm8PhDH9RrD5vr5x3OCq3cYavmhdd7jQq26lc7qhcx1m5TXS+aj+GEb7JMKpNS4Yq7w0ddHlk3rBZ03PENE2p8haeVtV0qPryyHT4cYVCVdPV1g+VlStUWq5QWbnMsnKFyisUKi1TqKwiPF9WrlBZ5bLyasui21SE58vDy8zI+tXXKa+5LNJGsKRU5f49CgZqJxK2eJfiOrZT8qBeOvbW36nTxWc29VONFsBmi/RCM9T2CNsIBk2VRgKyfWY0XCsNSOVlpirKpPJSU+XlUkVptWXV7vfvM1W0UyovD6m8VKooM1VRXrldmVRRFn7rHQ67U3I6JUdc3SGb0xX+XLPZJJu98mYLXyyhavqA5YdY17BJdnt4edV0ZLlRYx2jcht75brh6cjyetRlU43PVynyuUsQCADAgQjEAKCZOrqnXUf3rBp82zTDPTD27TFVvMdUYG/4ft+ekPYXSRUV4avUBSukinKz8l4KVoRPgaool4KR5RXh6YrK9YPV5ivKf6KopmSGT+kzZFbeV57iF06gKr/rmdH55JJN6pc/rdbyWvOV95G2Iu1bybDbZbgcsrmcsrkcssW5wvNOZ+1lLmd4eRu3nHGu8GOu8HqGyyF7nEtOT6pc7VLkSm8rlydFLk+qnJ5UORITrD5UxAi7PTImWuMFMaYZ/jwLh2NmNDQrL1fVdNkB96WV65ebda5TXhb+LCwPSaGgFIreh8L31ZaZIVPBYGRaClbeh4JmzW2Dhx/cNZZoLnbgHyUOtkw1/5AhHXydyB9Dau3nJ9qtvuzAOqoeNw4IASPTdYWUBw8u6wojjVrrHRA81nNZzbaNaIhpd1Tv9VjZc9EZ3hYAYD0CMQBoIQzDUEKb8JXqPJ0bbz+maSoUUs1wrbwyRKsM3SJBW1XoFt4ukjVFOlNVBEq1a9lq7ftmi/Z9+51Kf9gpGZKpmt0YDKdDNneC7IkJsrsTZHMnyOaOl83plGxGte4TldOGLfytxDBk2MLTdntQ8ceMUyTiMk2j8haOxUzTqLo3w49XlEtlZYbKS6WyUqms1FBZiRQyq76phSO18Ben1PY2eY62q0OGXRlehwy7Ee55UXkzDEk2m2xxlUFWZWBlOJ1VwVX1YKsy7DLsta86COCnGUa496vDKSmxeQcMoZAZDsuqhWShSHhWx7JIwFYVtlVuH6wevNVcHg3qqmf+UrVerdHOr9EHzaq/DRywjWpsU32dqm2qNjAPaK/Gvg7Sbn2Wmao6vgNDx5rPQ9Xy8jId8JzUDihrPffRx2ovMxthuEW7Q9FTfh0HnCrsdFYFaOHTgKt6MTqrnQ4cDdmckiOu5mM1ThmudvqxzU5vQQCojkAMAFCDYYT/ul2V0RzZP55Lt+3UsuMnqGL7LnkG9FTPk/sqacDQcG+ltklypibJmZYsZ9tk2RPiGqj6U352C6YZHj9pf5GpQLGp/UXhMZH27gxfvW+dL6iln5jqvtemP9yfIJvt8MZnAhB7bDZDskm1o2/CieYu8kciszIkCwYj02a16boDtUjP7PLI6cClCp8mXFazh2J5WdVpwNV7Le4vqn7KcO2ejcGKwzsWw6YDxteTXAk/dYVZrjgLoHUjEAMANIrirzbJcNg09OOnlTqkj9Xl1JthVF4dz22obYe61/lyWbn+/UiJFs0t11njXHI4+VIAAK1R5I9EtdNM6z/3Q8HKcfWqnzZcHjk1ODIeX83gLRzKRcbxk0r3V10sY0ehVNIAV5ytcbXZOpZFrjjrcFn/HAKIbQRiAIBGEQyUqGTLdsV3bm91KQ2uzwkOffetSx/8o0xfLC1XnxMcysxyqHtfu1zx/AMfAND4bHZDLrvkipcaI6ALBSuvOLuvca4463Ap2vusTaqh5DRDSWmGktsZSk6zKSnNUEo7Q0lpNiW0oUcagIZHIAYAwGGy2QyNGB+nvic6tPSVMn3+QYU++ne57E6pWx+7MrPsyuhtV1pHm1LSDdkZQBkA0MLY7FVjlzb0FWcjoVnJvvDQBMV7TO3dYapwU0hFO0PaX1yzHYdLVYFZmq0yNAuHZZHp5DRD7mSD4AxAvRGIAQBwhI7uadelf0qQaZratjmkb1YG9U1+hRYvLIv+Vdxml9q2N5TWyaa0jja16xieblc5707iH+4AgNbpSK84W14aHrtz786Q9u4I9zzbu9PU3h0h7d1p6sfvQtq7I6RA0QH7c6gyNKsdliW3C/c6a9fRpvhmfiEOAE2DQAwAgJ/JMAx17GZXx252nXKBS8EKUzt/MLWjMKSdP4RvOwpNbVkX1Kq8UI1xWeITFQ3H2nYw1CYl/Bdud7KhxGRD7qTKW7LBWGUAgJjgjDPUrpOhdp1++sI1FWWminaZ2rPDVNHOUGVoVjW96cvw/b49Zo3tktMMpXexKf1om9pXu09tb4QvggEgJhCIAQDQwOwOQ+lHG0o/uvY/5E0zfOWwHT+EtLMyMNvxg6mdhSGt/U9Q+/aatU4ViYhLCP+VvXpI5k42lBiZTqodpMUlilM2AQCtksNlqG2HyEVwal/HNaKi3FTxrnAvM//WkH7cEtL2LSFtXhvUZ++Vq6Is0p6U3rlmSBYJzuIS+F0KtDYEYgAANCHDiJw+YleXzLr/8R4MhsdZ2bdXCuw1FSgK3/btNcPzlcv27TG1fUsoOl9eWvc+7Q7JGSe54gw54yRnvCGnq3I+vtryOEOuysddB8w74ySny5ArvtryuKp5pys83gwAAM2Nw2kotb2h1PZSRu+av3tDIVO7t4d/n/74XSh6v/HLcu3dWdWzLMUT/kPX0T3tyvDa1LW3XcntfroHG4DmjUAMAIBmxm43lJhiKDHl8LYrLz0gOCsKXyGsvDR8pa/yUlPlZTXny0rDAxsX7ZTKSkMqL5XKSsPhWlnJwUO2ujiclcFbfFXAVj2Iqx7IHRi4ueLCPetsdlXdbFXzdrtk2CLT4eWGLbzcZguHcbbodO3tqy8DACDCZjOU1tFQWkebev+i5mMl+8LjlW3/LtyrbFtBSCs/KFfev8JBWWq6oYzedmV47era267Ox9rkjOP3DNBSEIgBANBKOOMMpcQZSvE0XJumaaqiTCorlcpLwgFaeZmp8pKq4CwSrIUfN6tCtWqhW3lpOKg7WCgXOV2lKdQO2Yw6QrfwlyTDXj10qyNkqx7G1Qjk6gjoDgz5bJJRucxeuSwa8h1hm4ZRdYswDEmGZFRbEFmmyF21bQ68j25bfXmN9g62T6PO9utq76fr1SHq5csngMYRn2ioSy+7uvSq2atsjz+kgq+C2vxVUAW+kN6ZX6ry0vDncJdMm7y/dMj7S4c6dbfxGQU0YwRiAADgoAwj0ttL0mFeJexwhIKmgkHJDEnBoBQKhpeFQpHpylvIPGC+cr2gotuHp8PLqubD65qV+6nRbqTNGvuqa99V+6q5fjjkM6utHwyFZFauEzkeM2RWO7aa2x+4fxy+ww3wqm/3U4FbVRtGzf1Ub/sQ2x4YdFbNVy6rFXLWsfyg4W1Vm9Ew124csI969KSsUUPNfYf3Z1Rrv47wttr6BABo7VI8NvU72aZ+JzslScEKUz9sCmmzL6hvPw/qgxfKtGhemVLTjWg4duwAO73HgGaGQAwAAFgu8qW6ptj94vBTwV+tMC1Uc5kkmWa1e1MyFVlQ9ViNxw9YFp03Fd6yens64PFa25o15g+2z4O2d4j2ozVVLjhw2SH3Wb3dasvqrqva83ZgPXXVeuBzFVmn+s+wMiStCkzrCG+DpirKDgxQ63pN1P0aqCvQrf58NDbDdmDoVjs4qx6yHfK0aHt4DKj6jHUYPUW7cqxExjpEU7A7DHU+1q7Ox9p14qjw1S83fBGU738VWvvfCv3njXI546S+Jzp08vmuWuOYAbAGgRgAAEAzY7OFe+jIWX0pX+Rx5EIhs6oXY7XgLFgrjDtYb8Y6AtnQIULaGj03zWrt1+7Jeah9HzjWYXlZtbERS+sf+NU11uGBFwlxVRsDsa4ALhq8RS9KUnMdh4tecrHO4TKUOdihzMEO/foP4QH71/ynQsvfLtfK6wPq0sumk85zacApDjlcvFYAqxCIAQAAAK2czWZItvBVZ6u0ji/ipmmqolw1xjaMjGNYr7EOq41rGNhranf1bY5grEObXXInGXInG+H7JEOJyVJCkqHE5JrL3clVy5wEI62SYRjqkGFXhwy7si9y6atPg1r6apn+cV+J3njG0OmXuHTSeU4u+gJYgEAMAAAAQItlGJWnR7okdyOGfKFQ5UVGSsw6r8hbVmqqolTavy98ld/I1X4De01t2xKZl/YXmXX2aHPGqWZIFgnTUsNXQGxXeSXEFI/BqZ8tlM1uqM9Qh/oMdWhbQVAf/btcrz1VqjX/qdAlU+LVtr3N6hKBmEIgBgAAAACHYLOFT6t0xf+8MCoUMlVSLO0rMrW/KHwF3sDeqgBtX1HV/I7CkIp3m9q7oypEszuktu0NpXWyKa2DTWmdDLXrZFNax/DNnURY1hJ0yLBrzI12DRrm0AsPlOjBift0/qR4DT7DeeiNATQIAjEAAAAAaCI2myF3suQ+jCv3lpeZ2rXN1M4fQtr5Q0g7CkPa+YOpzV8FtTIvpNJA1boJbaS0jjZ1PtauzCy7jh3kUGIjXiUYP8+xAx3641OJevnxEv3jvhIlpxnqOYiv6UBT4J0GAAAAAM2Y02WofRdD7bvUPqXONMOnYu4sjARlIe0oNLVpTVD/W1Quw5A6H2tTzyyHMrPsOqa/nfGqmpmENoYu/VO89u7Yr38+WKLJTyUqPpGfEdDYCMQAAAAAoIUyjPCg/YnJdnXpZa/x2O4fQ/pmZVDf5Ffo03fK9cELZeoxwK4xN8arXSfGq2pObDZDF/8xXg/+YZ/eea5U510db3VJQKtHIAYAAAA0EwUFBfL7/VaXgVbE7pF6nyX1OlP64Vu3/vdye838Q5nO/8tGOZx1jO4PS3Xp115f/DdeXX651upS0Mp5PB5lZGRYXYalCMQAAACAZqCgoEBer1eBQODQKwNHqE3cUUpwtdM9r31hdSmoQ0pCVxmy69bnNlhdClo5t9stn88X06EYgRgAAADQDPj9fgUCAeXm5srr9VpdDtBsTJ8+XTt37tT06dMVCoW0Y8cO/ec//9HcuXM1aNAgPfjgg3I4+GoL1JfP51NOTo78fj+BGAAAAIDmwev1Kisry+oygGajXbt2stvtOuuss6LLLrvsMo0ePVpnnHGGVq9erSuvvFIFBQW69tprtXjxYtlsNo0YMUKPPvqoOnToYGH1AJorRlIEAAAAALQ4p59+ugYMGKCXXnpJoVBI5513nnbu3KkPP/xQ7733njZs2KBLLrnE6jIBNFP0EAMAAAAAtEi9e/fW6tWrtXjxYn3xxRfauHGjunTpIkl67rnndNxxx+nTTz/VL37xC4srBdDc0EMMAAAAANAimaYpwzDk8/nUpUuXaBgmSX369FFqaqp8Pp+FFQJorgjEAAAAAAAtks/nU/fu3a0uA0ALRCAGAAAAAGhxlixZoi+++EIXXnihvF6vtmzZoi1btkQfX7t2rXbv3q0+ffpYWCWA5ooxxAAAAAAAzVppaal++OEHBYNBbdu2TYsWLdI999yjkSNHaty4cbLZbOrXr58uu+wyPfzww6qoqNA111yj0047TUOGDLG6fADNEIEYAAAA0AIVFBTI7/dbXQZQy8qVK5WcnKwePXo0SHs7duzQokWL1KlTJ9ntdiUnJyszM1OTJ0/WyJEjtWrVKknSnXfeqfvvv18nn3yybDabTjjhBP35z39Wfn5+g9QhSStWrFBaWhqnaaJZ8ng8ysjIsLqMFoNADAAAAGhhCgoK5PV6FQgErC4FaFLBYFC7du3S8uXLtXz5cs2YMeOg677//vt6//33m7A6wFput1s+n49QrJ4IxAAAAIAWxu/3KxAIKDc3V16v1+pygBrGjh2rAQMGaOrUqVaX0uAuuOACDRs2TNddd53VpQA1+Hw+5eTkyO/3E4jVE4EYAAAA0EJ5vV5lZWVZXQZQQ0JCgtLT01vlazMuLk4dOnRolccGxBquMgkAAAAAAICYQiAGAAAAAACAmEIgBgAAAAAAgJjCGGIAAABAK1dQUCC/3291GYgRycnJqqioUH5+fqPva+vWrRo1apQWLFigXr166bPPPtPEiROVl5enpKSkBt9fWlqaSkpKmuTYAEnyeDwMkt9ICMQAAACAVqygoEBer1eBQMDqUhBDPvroIz399NNNtr+xY8fWmM/Ozm60fS1fvlyPPPJIo7UPVOd2u+Xz+QjFGgGBGAAAANCK+f1+BQIB5ebmyuv1Wl0OEFVeXi6n0/mz2jiwhxjQmvh8PuXk5Mjv9xOINQICMQAAACAGeL1eZWVlWV0GYlh2drb69u0rh8Oh3Nxc9evXT48++qj+9Kc/6eOPP1ZiYqLOOussPfTQQ/J4PJKkRYsW6c4779SXX34pu92uE044QbNmzVKPHj0khU9hlMKv74EDByovL0/Dhg3Trl27lJqaquzsbH344Ye1atm4caO6deum3bt3a8qUKXr11VdVWlqqIUOG6KGHHtKAAQOa7okBYAkG1QcAAAAANIn58+fL5XJp2bJl+utf/6rTTz9dgwYN0meffaZFixZp27Ztuvjii6Pr79u3T3/84x/12WefafHixbLZbLrgggsUCoXqtb+XXnpJhYWF0dvo0aPVq1cvdejQQZI0ZswYbd++XW+//bZWrFihrKwsnXHGGdq5c2ejHD+A5oMeYgAAAACAJtGzZ0/dd999kqQ777xTgwYN0t133x19fM6cOerSpYu+/vprZWZm6sILL6yx/Zw5c5Senq61a9eqb9++h9xfpAeZJD300ENasmSJli9froSEBC1dulT/+9//tH37dsXFxUmSHnjgAb3yyit68cUXddVVVzXEIQNopgjEAAAAAABNYvDgwdHpVatW6YMPPlCbNm1qrbd+/XplZmbqm2++0W233ably5fL7/dHe4YVFBTUKxCLePvtt3XTTTfp9ddfV2ZmZnT/xcXFateuXY119+/fr/Xr1x/J4QFoQQjEAAAAAABNIjExMTpdXFysUaNG6d577621XqdOnSRJo0aNUteuXfXMM8/oqKOOUigUUt++fVVWVlbvfa5du1aXXnqp/vrXv+qss86qsf9OnTopLy+v1japqan1PygALRKBGAAAAACgyWVlZenf//63unXrJoej9lfTHTt2aN26dXrmmWd0yimnSJKWLl16WPvw+/0aNWqULrzwQt1444219v/DDz/I4XCoW7duR3wcAFomAjEAAAAAUQUFBfL7/VaXgVaouLhY27dvV35+viTp1FNP1ZNPPqkRI0Zo/PjxSk5O1pYtW/Tuu+/q1ltvlWEYSklJ0V//+lft3LlThYWFeuyxxySFT6nMz8/X1q1bJUk+n0+hUEhff/21pPDpkElJSfr9738vwzB04YUX6t13343W0rZtW6Wlpalfv34aPny4rrvuOnXt2lU//vijli5dqmHDhqlPnz5N/AwhVng8HmVkZFhdRswjEAMAAAAgKRyGeb1eBQIBq0tBK7VixQotXLiwxrLFixdr8eLFNZa9/vrr0em33npLb731Vo3Hp0yZUmN+7NixNeazs7NrzI8cOfIn65o0aVKN+Tlz5vzk+sDP4Xa75fP5CMUsRiAGAAAAQFL49LJAIKDc3Fx5vV6rywEO6b333tNNN92kvLw8JSUlWV0OcEg+n085OTny+/0EYhYjEAMAAABQg9frVVZWltVlAIcUuRrkwIEDlZKSYnE1AFoSm9UFAAAAAAAAAE2JQAwAAAAAAAAxhUAMAAAAAAAAMYVADAAAAADQIsybN0+pqalWlwGgFWBQfQAAAABHpKCgQH6/3+oyEEM2bdqkYDCo/Px8SVIoFNLll1+utWvXKi4urt7tDB48WA888ICGDRvWWKUixng8Hq4a2cIQiAEAAAA4bAUFBfJ6vQoEAlaXghg0ePDgGvNz58497DamTJnSUOUAcrvd8vl8hGItCIEYAAAAgMPm9/sVCASUm5srr9drdTlowT766CPdeuutWrJkiex2u9atW6exY8dq/Pjxuu666yRJd9xxh8rKynT88cdr5syZuueeezRz5kxt27ZNAwcO1PTp05Weni5JWrNmjR5//HF99dVXqqioUK9evfTHP/4x+jodOXKkCgsLo/vv1KmT3njjjaY/cLQaPp9POTk58vv9BGItCIEYAAAAgCPm9XqVlZVldRlowXr06KHJkyfLMAxlZWXp448/lsfj0VdffRV9ba1Zs0ZTp06Vw+FQaWmpXn31Vf3rX/+SzWZTTk6OnnvuOT3//POSpN27d2vSpEkaMmSITNPUzJkzNXnyZH3zzTdKSkrSqlWr1L59e82dO1cjRoyQ3W6PhmkAYgeD6gMAAAAALJOSkqKBAwcqLy9PkpSXl6cbb7xRK1euVHFxsb7//nt9++23Ou200yRJ5eXleuqppzRkyBBlZWVp0qRJWrx4cbS9008/XTk5Oerdu7e8Xq9mz56tQCCgDz/8UJKi4Vdqaqo6duxIGAbEKAIxAAAAAIClTjvtNOXl5ck0TX388ccaPXq0vF6vli5dqg8//FBHHXWUevbsKSk8VlOPHj2i23bq1Enbt2+Pzm/btk2///3v1bNnT6WkpCg5OVnFxcUqKCho8uMC0HxxyiQAAAAAwFLZ2dmaM2eOVq1aJafTqd69eys7O1t5eXnatWtXtHeYJDmdzhrbGoYh0zSj8+PHj9eOHTs0a9Ysde3aVXFxcTrhhBNUVlbWZMcDoPmjhxgAAAAAwFKnnHKKioqK9NBDD0XDr0gglpeXp+zs7Hq3tWzZMl133XU655xzdNxxxykuLk5+v7/GOk6nU8FgsCEPAUALQyAGAAAAALBU27Zt1b9/fz3//PPR8OvUU09Vfn6+vv766xo9xA6lZ8+e+vvf/y6fz6fly5frsssuU0JCQo11unXrpsWLF+uHH37Qrl27GvJQALQQnDIJAAAAoNEVFBTU6qUDVOf1evX5558rPT1d+fn5kqTu3btrx44d2rdvn/Lz87Vp0yYFg8Ho45K0fv16SYoumzJliu666y4NGjRIHTp00P/7f/9P3377rb777rvoOtdcc40efPBBzZ49W+3bt9cbb7zRxEeLlsDj8SgjI8PqMtBICMQAAAAANKqCggJ5vV4FAgGrS0ELcNFFF9VaNnjw4J+cP9iygoICTZ06VZI0c+ZMzZw5s9Y6hYWFdW4LuN1u+Xw+QrFWikAMAAAAQKPy+/0KBALKzc2V1+u1uhy0Iu+9955uuukm5eXlKSkpyepy0Ir4fD7l5OTI7/cTiLVSBGIAAAAAmoTX61VWVpbVZaAViZwuOXDgQKWkpFhcDYCWhEH1AQAAAAAAEFMIxAAAAAAAABBTCMQAAAAAAAAQUxhDDAAAAECzUlBQIL/fb3UZaAE2bNggSfr8888ZVB/15vF4GCgfBGIAAAAAmo+CggJ5vV4FAgGrS0ELkp2dbXUJaEHcbrd8Ph+hWIwjEAMAAADQbPj9fgUCAeXm5srr9VpdDpqJ6dOn69NPP9W+ffv0zjvvKD4+XpJUWlqq4cOHKzExUUOGDNGMGTNqbVteXi6n09nUJaOZ8vl8ysnJkd/vJxCLcQRiAAAAAJodr9errKwsq8tAM9GuXTsNHTpU69ev18aNG3XZZZdJkhYsWKDu3bure/fuSk1NVVZWlrKzs9W3b185HA7l5uaqX79++uCDDyw+AgDNDYPqAwAAAABahCuuuEJz586Nzs+ZM0eXX355rfXmz58vl8ulZcuW6amnnmrKEgG0EARiAAAAAIAWIScnR0uXLtXmzZu1efNmLVu2TDk5ObXW69mzp+677z716tVLvXr1sqBSAM0dp0wCAAAAAFqE9PR0nXvuuZo3b55M09S5554rj8dTa73BgwdbUB2AloRADAAAAADQYlxxxRWaNGmSJOnxxx+vc53ExMSmLAlAC0QgBgAAAABoMUaMGKGysjIZhqHhw4dbXQ6AFopADAAAAADQYtjtdvl8vug0ABwJAjEAAAAALVZBQYH8fr/VZdTL5s2b9eOPP2rIkCFWl1JvHo9HGRkZVpdRS3JystUlAGjhCMQAAAAAtEgFBQXyer0KBAJWl9Jqud1u+Xw+y0OxefPm/eTjr7zySnQ6Ly+vUWsB0DoQiAEAAABokfx+vwKBgHJzc+X1eq0u55CeeOIJvfXWW3rjjTesLqVefD6fcnJy5Pf7LQ/EAKChEYgBAAAAaNG8Xq+ysrKsLuOQOnbsKJfL1SJqBYDWzmZ1AQAAAAAAAEBTIhADAAAAAABATCEQAwAAAAAAQExhDDEAAAAAMaGgoEB+v9+y/e/fv18ej0f5+fmW1SBJHo+HQfIBxDwCMQAAAACtXkFBgbxerwKBgNWlaPDgwZbu3+12y+fzEYoBiGkEYgAAAABaPb/fr0AgoNzcXHm93kbd1/Tp03XUUUdp4sSJtR4rLy+X0+ls1P3/FJ/Pp5ycHPn9fgIxADGNQAwAAABAzPB6vcrKymrUfbRr106dOnVSVlaWunXrpt/97nf65ptv9Morr2j06NGaN2+eli5dqptvvlmfffaZPB6PLrjgAt1zzz1KTExs1NoAAGEMqg8AAAAAjeiBBx7QgAEDtHLlSt16661av369RowYoQsvvFCrV6/WCy+8oKVLl2rSpElWlwoAMYMeYgAAAADQgObNm1dj/vTTT9fkyZOj81deeaUuu+wy3XDDDZKknj176pFHHtFpp52mJ598UvHx8U1YLQDEJgIxAAAAAGhEQ4YMqTG/atUqrV69Ws8//3x0mWmaCoVC2rhxY6OPcQYAIBADAAAAgEZ14LhgxcXFmjhxoq677rpa6zLQPQA0DQIxAAAAAGhCWVlZWrt2rY499lirSwGAmEUgBgAAAAAHKCgokN/v/9ntlJWV6bvvvlN+fn502a9//WuNHz9el1xyic4//3wlJCRow4YNWr58uaZOnXpE+/F4PM22d1lDPZfAT2nO7wE0TwRiAAAAAFBNQUGBvF6vAoFAg7Q3c+ZMzZw5s9byf/7zn/rnP/9Za9mRcLvd8vl8zS4QaOjnEjiY5voeQPNFIAYAAAAA1fj9fgUCAeXm5jboAPdPPPGE3nrrLb3xxhsN1qYk+Xw+5eTkyO/3N7swoDGey127dunMM8/UzJkzlZ2d3SBtNheFhYUaOXKkHn/8cQ0dOtTqchrUhg0bNGbMGM2ZM0cDBgxo0Lab83sAzReBGAAAAADUwev1Kisrq8Ha69ixo1wuV4O22VI05HMZOf2yR48ere653Lx5sySpZ8+ere7Y4uPjJUm9evVqdceGlslmdQEAAAAAAABAUyIQAwAAAIAmlp2drRtuuMHqMtDMTZgwQeeff36jtd+tWzc9/PDD0XnDMPTKK6802v6A5oRADAAAAACa2EsvvaT/+7//s7oM1MEwjJ+83X777VaXCKABMIYYAAAAADSxtLQ0q0vAQRQWFkanX3jhBd12221at25ddFmbNm2i06ZpKhgMyuHgqzXQ0vCuBQAAAIA6vP/++xo/fry+/fZbud1uDRo0SK+++qrWrl2rv/zlL1q5cqXKy8vl9Xp17bXXRq+iOGPGDO3cuVOzZs2KtlVeXq5Zs2bpl7/8pfLz83XVVVcpMzNTU6ZMkSSNHDlSo0eP1pYtW/T+++8rKSlJN9xwg2666aZoG5988omuueYaffXVV+rbt69uueUWXXDBBVqwYEHTPjGNqKCgIDpo/sHs379fl19+ucrLy5Wfn9+o9ezatUuhUEhbt26VJH322WeaOHGiHnnkET3xxBP69ttv9fjjjysrK0vz5s3Tyy+/rB07digjI0NXXnmlzjzzTElSMBjUXXfdpU8//VQ7duxQx44dddFFF2ns2LHRfQWDQd13331yuVwaPXq0Ro8erR07dqi4uFj5+fn66KOPdOutt2rJkiWy2+1at26dxo4dq/Hjx+u6666TJN1xxx0qKyvTnXfeKUlauXKlHnvsMfl8PqWmpio7O1vXXnutEhISJEllZWX67rvvajyP69evb5TndefOnbr88su1a9euerfv8Xi4aiQaDYEYAAAAANThL3/5i+6//35dcMEFKioq0scffyzTNFVUVKTx48fr0Ucf1ffff6/hw4crJyen1vaDBw+utWzx4sXR5StWrNDChQujjz3++OPR6UAgoJtvvlm//OUvNWzYMO3du1ejRo3SOeecowULFmjz5s2tbgyygoICeb1eBQKBeq0/d+7cRq6oyoE/y0gAJUkTJ06stf4333yjqVOnHrS9zZs3a+bMmZo5c2adj5eVlem5556rc//HH398jXXnz5+v+fPn11j29ttv12pz27ZteuGFF/TCCy/UWH5gHZGQtrEczs/N7XbL5/MRiqFREIgBAAAAQB2CwaBGjx6trl27SpL69esnSTr99NOj6wQCAQWDQcXHx+uee+7RqaeeKkkaM2aMRo4cqfHjx0uSbrzxRqWkpETHn6qrh9igQYOi44qtXbtWv/3tb/Xee+9p2LBhWrBggQzD0DPPPKP4+Hj16dNH33//vX7/+983yXPRFPx+vwKBgHJzc6O97az22muvaebMmfrwww8lVfUQmzlzprKzsyWFw6thw4bpySefVP/+/aPb3nHHHSopKdHdd99dZ9v33nuvduzYofvuu0+SNHz4cF122WUaN26cJKmiokK//vWv1bt3bz344IOSpMsuu0zDhw/XuHHjNHnyZB133HGaPXu2lixZouLiYp199tl6+eWXlZGRoTvuuEN2u13Tpk2L7nPlypW66qqrtHTpUsXFxWnkyJEaO3ZstKfa4MGD9cADD2jYsGEN+0QeAZ/Pp5ycHPn9fgIxNAoCMQAAAACow/HHH69+/fpp+PDhOuuss3TRRRepbdu22rZtm2655Rbl5eVFx5sqLS2Vy+VSVlaWJGnSpEmaPXu2Zs2apW3btumTTz7RkiVLoo+3adNG7du3j867XC5lZ2dH5yN27twpSVq3bp369++v+Pj4GvW1Rl6vt9bzYJXVq1fLbrdH69m7d68k6ZJLLlHnzp0lSWvWrFFJSYkmTZpUY9uysjINGjQouu3jjz+uOXPmqKCgQPv371dZWZkGDhyorKws7dmzR36/X6NHj65x7CeccIJM04wuO/vss/X1119r0KBB+uKLL/Tkk09q6dKlKi4u1s6dO3XUUUdFr0r5/fffa/Xq1XrnnXei7ZmmqVAopNTUVHm9XrlcLh199NE19tmjR49m8/wDjYlADAAAAADq8MQTT6ikpETvvvuuHn30UU2bNk3Lly/X1f+/vbsPkuSs7wT/y3rpl5oZTUsqSSMklSQwK5CskTTIxmfWt3AebEOsX7ARBNxgBNxql/CL8MIZhV/E4ViwcZiTuHMI7+JDEjZhFvwSd4B8Bq1DZ45bCPCchPA1yNIgBEIaqTXvMz39Upn3R1ZVV/XLvGlmqrufzyeiIjOffPLJX9VU53R+Oyvrne+M5557Lj7ykY/E4cOH4w1veENs3Lgxdu3a1bs30nXXXRePPfZY3HPPPfHQQw/FC17wgtiwYUNv/aFDh+KZZ57pLc/Ozsbu3bt7y5OTkxFRBhip6T731eDxxx+Pdrvd+3d55JFHIiLi0Ucfjd27d0dExMMPPxwREXfccUdceOGFA9vX6/XYuXNn/N3f/V28//3vj3e9612xdevW2LBhQ3ziE5+Ib37zm7Fz5844ePBgb/z+m/bv27cviqLo7f+yyy6Lj33sY/GpT30qIsorFK+++ur41Kc+FQcPHoxrr72213dqaipe97rXxZve9KYlz+vAgQOxc+fOs3oPsZO1mt4HrE8CMQAAgGVkWRaveMUr4hWveEXcfvvtcfnll8ff/M3fxJe//OW466674rWvfW088cQTMTY2FgcPHlz2nlBve9vbevOL70O1+B5ii7fPsiwajUZERFx11VXx53/+5zEzMxOjo6MREfG1r33ttD/nYWo2m9FoNJa9H9uwLf63635cst8tt9xy3HE+9KEPHXPslT4Cu3j//R9xXKz/irDHH398yT3DFjvb9xA7GY1GI5rN5rDLYJ0SiAEAACzj4x//eOR5HhdeeGF89atfjWeffTZe+tKXxotf/OL4sz/7s7jxxhvjwIEDsXXr1njooYfiV3/1Vwe+NfArX/lKvOtd74o8z+Pzn/98fOYzn4n77rsvPve5zy17D7H+ezlFRLzlLW+JzZs3R0QZgPz2b/923HLLLXHbbbfFE088EX/0R38UEWVwth60Wq2YnJw87rdM7t27N7Zv3z5wH68zZaV7iD3wwAOxadOmXr+77ror/vIv/zJ+4zd+I66//vo4dOhQPPTQQ7Fhw4b42Z/92fiLv/iL+OhHPxp/8Ad/EJdcckl8/vOfj0996lNxySWX9ELRe+65J+655544ePBg/O7v/m5885vfjC984Qtx44039u4hFlG+Fx599NH4zd/8zXj9618f+/fvj5/6qZ+K+fn5+Ku/+qu44oorIqK8sf9b3/rW+Pmf//n4hV/4hRgfH49du3bFV7/61d4N/8/mPcR27doVN910U3z84x+P66677oS28S2TnEkCMQAAgGXs3LkzPv3pT8eBAwfi8ssvjw9/+MPxmte8JrZs2RK33HJLbNu2LS677LL44Ac/GO95z3uW3IvphhtuiD/8wz+Ma665Jn76p386vvSlL/XuM7bcPcQWb1+v13vz55xzTnz2s5+Nd77znXH99dfHtddeG7fffnu8+c1vjpGRkbP3opxhrVbruAFINzA7G/e6WukeYtddd11MTEz0+n3sYx+La6+9Nj760Y/GBz7wgZiYmIht27bFb/3Wb8W2bdvimmuuiampqfid3/mdyLIs3vSmN8Wv/dqvxd/+7d/2xt66dWscOnQo7r777rjjjjvilltuiV/6pV+K/fv3DzzPn/mZn4mPfOQj8cu//Mvxkpe8JCIirrnmmti9e3f84i/+Yq/ftm3b4oUvfGEvSC2KIl70ohfFG9/4xmO+787U69q9/91VV13lHmWsCgIxAACAZfzxH//xsifuN9xww5KPK77+9a9f0u/w4cOxd+/eeMc73rFk3QMPPDCw/Pjjjy/p8+CDDw4s//iP/3g89NBDveVPfvKTUa/XY8uWLcd4FjwfN998c9x888295Ve+8pXL3tcty7K49dZb49Zbb112nNHR0bj77rvj7rvvHmj//d///d58rVaL973vfXH33XfHX//1X8erX/3qZce6884748477xxoW/xe6fqRH/mR+MIXvrDsuoil77sU71lHugRiAAAAp1Ge5zE1NRUf/vCHY2JiIn7u537utIz7iU98Il74whfGJZdcEg899FC8973vjTe84Q0D3zwJwIkRiAEAAJxGTzzxRFx55ZVx6aWXxj333BO12uk57Xr66afj9ttvj6effjouvvjiuOmmm+IDH/hAfOtb3zot4wOkRCAGAACwjMnJyVPe9h//8R978zt37oyIMtCanZ3tLZ+s7du3x/bt2wfavvWtbz2vOs+W01nj3r17IyLiscceO+XXcrV66qmnIqK8If75558/5GpOr127dkVExLe//e3TflXjWvgZYPURiAEAAPRpNpvRaDRix44dZ2T8l73sZad9zEajEc1m87SP+3ydydfy3e9+92kfc7X4lV/5lWGXcMa8/e1vPyPjrtafAVYvgRgAAECfVqsVk5OTvW8zXAuazeZxv51xGNbia8natFp/Bli9BGIAAACLtFotJ9enidcSWI0qwy4AAAAAAM4mgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASakNuwAAAICzZXJycmj7np2djXa7HePj40OrYZjPH2A1EYgBAADrXrPZjEajETt27Bh2KUPXaDSi2WwOuwyAoRKIAQAA616r1YrJycmYmpoaWg133XVX3HffffG5z31uaDVElOFgq9Uaag0AwyYQAwAAktBqtYYaBG3ZsiVGRkZi27ZtQ6sBgJKb6gMAAACQFIEYAAAAAEkRiAEAAACQFPcQAwAA1rTJyclhl3BCnn766ZidnY2dO3cOu5QTslZeV4BTIRADAADWpGazGY1GI3bs2DHsUk7Ky172smGXcMIajUY0m81hlwFw2gnEAACANanVasXk5GRMTU0Nu5R1q9lsDvWbOQHOFIEYAACwZrVaLYENACfNTfUBAAAASIpADAAAAICkCMQAAAAASIpADAAAAICkCMQAAAAASIpADAAAAICk1IZdAAAAwGKTk5PDLoE14Mknn4yvf/3r8drXvjbq9fqwy2ENcGyhSyAGAACsGs1mMxqNRuzYsWPYpbCG/N7v/d6wS2ANaTQa0Ww2h10GQyYQAwAAVo1WqxWTk5MxNTU17FJYA774xS/GbbfdFg888EBs2rRp2OWwRjSbzWi1WsMugyETiAEAAKtKq9VyssoJeeyxxyIi4vrrr4/NmzcPuRpgLXFTfQAAAACSIhADAAAAICkCMQAAAACS4h5iAADAWTE5OTnsElhn9uzZE9dff308/PDD0Wg0hl0O64jj1fonEAMAAM6oZrMZjUYjduzYMexSWKd+4id+YtglsA41Go1oNpvDLoMzRCAGAACcUa1WKyYnJ2NqamrYpQCcsGaz6Rtv1zGBGAAAcMa1Wi0nlgCsGm6qDwAAAEBSBGIAAAAAJEUgBgAAAEBSBGIAAAAAJEUgBgAAAEBSfMskAABwyiYnJ4ddAsBQOQ6uTQIxAADgpDWbzWg0GrFjx45hlwIwdI1GI5rN5rDL4CQIxAAAgJPWarVicnIypqamhl0KCfviF78Yt912WzzwwAOxadOmYZdDwprNZrRarWGXwUkQiAEAAKek1Wo5AWSoHnvssYiIuP7662Pz5s1DrgZYS9xUHwAAAICkCMQAAAAASIpADAAAAICkuIcYAAAwYHJyctglwAnZtWtXREQ8+OCDbqrPmuD4unoIxAAAgIgovyWt0WjEjh07hl0KnJRXvvKVwy4BTlij0YhmsznsMpInEAMAACKi/NbIycnJmJqaGnYpAOtWs9n0Db2rgEAMAADoabVaTtQAWPfcVB8AAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEhKbdgFAAAAZ97k5OSwSyAR09PTUa1WY2RkZNilnHbr+bmx+jhun1kCMQAAWMeazWY0Go3YsWPHsEsB4CQ1Go1oNpvDLmNdEogBAMA61mq1YnJyMqampoZdCol485vfHNddd128973vHXYpp93rXve6eNWrXhW//uu/PuxSSESz2YxWqzXsMtYlgRgAAKxzrVbLCRVnzfj4eFxwwQWxbdu2YZdy2o2OjsZFF120Lp8bpMZN9QEAAABIikAMAAAAgKQIxAAAAABIinuIAQDAGjU5OTnsEmCJ6enpePbZZ2Pnzp3DLuW0m5mZid27d6/L58ba5v+DkycQAwCANabZbEaj0YgdO3YMuxRY1re//e349Kc/Pewyzoh777037r333mGXAUs0Go1oNpvDLmPNEIgBAMAa02q1YnJyMqampoZdCgCrRLPZ9I3CJ0EgBgAAa1Cr1XLiAwCnyE31AQAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApNSGXQAAALBgcnJy2CXA8/L9f9oQYxvb0bz86LBLOe2eeHhjbJiYi/Mvmxl2KXDK/D9TEogBAMAq0Gw2o9FoxI4dO4ZdCjwvb/mxL8UP9n01/su33jPsUk67t79iZzz6zGfjH/75fcMuBZ6XRqMRzWZz2GUMlUAMAABWgVarFZOTkzE1NTXsUuB5+dv/5bL4l5dfHn/4868adimn3ef+qBWveM1b447X/OthlwLPS7PZjFarNewyhkogBgAAq0Sr1Ur+BIW17/9qHI4LLtgU27ZdNuxSTrsvjh2Kiy7aGNu2XT7sUoDnyU31AQAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEiKQAwAAACApAjEAAAAAEhKbdgFALA+jW45Pzb8i1bsfP1tMXbJhVE/d1PUz90Utc0by/mJ8lGb2BiV0ZGo1GtRGalHNlKPysgy8xV/wwGAM6koimjPR/mYi5ifL3rz7fki5jvzR48UcfRwEdMHizhyqIijhyKOHCpi+lARRw4WcWhfEZf8kP+3gdVNIAbAGbH5hqvixe/7N7H7//iHmNuzP44++UzM7T0Yc/sOxvy+g1HMt09qvKxWXSYwq0WlXo9Kp61c15nvto/2t9XKYK2SDU6zLLIsi6hUIqssM80G+x9r3cCYlSwiG+zfXbd0rM62fesG6shisF/fuojoLB+jjtP1nLLsDLxbgNQVRRFFEVEUEVFEFHn0lvPufN5Z1+nb397bbkl7sUJ7Z7zothcD7d19L+5f9O3/uP2LiCIvTu+Y/bXGYHu7HdGeWwi05jsh1rHCrfm5wW3a8yf375ZlEWMbIsY2ZNHYlMXYhizGN2bx8tfU48ZX10/vmwTgNBOIAXDGXHzTT8bFN/3kkvaiKKJ9eDrm9h2K+f2HIp+di2JuPvKZ2chn58vl2bnefD4331nutM3MRtFbN7cw31nf7ds+Mh1z+w70rZ+PyPPyBCkvosjzclrkvfkoivIEprsuzzvbRDnNi0XTvHPy1te/KM7+i302VU405OuGednScK07jWzpWJVKRBYL43YMZHH9CyvMn0j/bMVxTqHvMfucev9shW0XPcGT6r/sfrNs8OqQdufEub1wotwLHSKiKLIooruc9UKHbtvcpVfH0/EvIsuyKLphat9jcDkiskpU6tXI6vXIOleMVupluJ2N1CKrVgae0MCPWd/84I9fsbS9WG7tymOs9ON8Yvs/gf4nUMNK/Yti+RXFcm2nuP+lYdRCIJP3hUr9wUy3vXy/DAY9eV/Qszj4Wq+yLMpjWvk3kt7bvnMIXPgxqGS9PpXltum0Z5WI8tjZtz4iqrXyUatnUa2X8/XRLMY3dtdVolqPqNUiqrUsavXo9avVFrbprVvSr5yvVCPGGmXwNToeUan6QwmwNgnEADjrsiyL2sZG1DY2Ii69cNjlnHZF0Q3WlgnLumFaJ3jrhWqd9d113X6D2xadMK8vkCuK5UO+vj7Lh3wL4d1xQ77iOAHgQD1xjOc02L8XHhaD9Q5MF17UZedXCgRW7nMS/Vfc5/LbPZ/+J/I8TvY5da+MGVhRRMwcyePIoYijh4qYPVpEu11E3rlypAw3lo6XVSKq1WLhxD6KiOicvEfRCbS67eXP+OzEXByp1GIhRem8dwbSkBhsn5+Pot1e+jpEUYYF9VpUatVyOjoS9YkN5UevN2+MSrXv41nL533dIlfKEgfbY/n2E+lzsmOeev/sGBnpCuHw8fbf39wfuAwEN+W/cTeIWdJvSf9s6fplt8uWb192PjtGbYvbs+VrXLKv7ATHW/qcup/qX2n/AKw+AjEAOM26V7647xmrybNP5vHJD07Hk4/mUZmIuHhbJS68rBKNTeWVHuObsoX5jVHOb8qisTGL+ujZO6HP5+Zjbt/BmNtzoPyI9d6D5cet9x7ofOy6nE5/9+nYf99ktA9PR705ET9813tjyy/8q7NWJwCwtgnEAAASsOsb81GrR7zjP4zHlT9cjdHx1XnVSqVei9ELzo3RC849bt98fj4OPvxY/PP7/zSe/T//H4EYAHDC/OkaACABzz1VfvPbS36ktmrDsJNVqdVi8w1XRePKi2P/1yaHXQ4AsIYIxAAAAABIikAMAAAAgKQIxAAAAABIikAMAAAAgKQIxAAAAABIikAMAAAAgKQIxAAAAABIikAMAAAAgKQIxAAAAABIikAMAAAAgKQIxAAAAABIikAMAAAAgKQIxAAAAABIikAMAAAAgKQIxAAAAABIikAMAAAAgKQIxIA1bz5vD7sEgFWvPV8Mu4QzqsjX9/MDVoeicKyB9aI27AIATlW1Uok3br0x3vXZ/xwf+fLfxwvO2RwXb9ocLzhnIrZsOic2jozFeL0ejfpIjNfrMV4f6c03RkZivLbQXq34+wCwurXbRczNRMzNdKazg8uzM0XMz0bMzkTMHCni4J4i9j+Xx4Hnyvnnnsrjv/2l+rCfxhlx/qtfHk99+r/EP1z7phhvXRRjl10U45dtidEt50W1MR7VDWNR3dCZdpZrveWxyKrVYT8F4CzL20XMHo2YPbrMdGZh/uCeIvY9W8TeZ/LY90wes0ezePENfm+E9SArRNxwTLP/322RjV4Q9Re9e9ilsIxHnt0dn3n46/GDA/vjqYP74wcH9sUPDuyLpw8diLn2iV85NlKtDYRn5XQkRqrVqFYqUc0q5bRSiWqWRSWrRLWSLWovp5Ws254NbptVolLpW7dsezl+/5iVrH+cbIV9LTPmonG66yuL+vVvO7ivwXq6+6oIDzmN8ryIIo/I8yin7Yii6C4Xkbf71g1Mi8iX3a7oLS/drlhmnEXjtfvaiu7yon11pt0x+2voblcuFyvsa7CWbu3d5fnZhcBrdiZifqacnszFsLWRiHPOy+Kc8yudaRYTF1Tihv+uFuect/5+htszs/Hkn90Xh7/13Zh+Yncc/d7TMf293TH77L7yxT2OytjIQmA23gnPxkcjq1bKR61ahmaVSlRq1YhqJbJqtdM+2GdhvtOn2jffaY9qd5xj9F9pvIF91hbmO9sujH2c/v3jO64nafFxKM8jinbnuNZe1N53TOtva7f7jrnLtR9vjN5+Fo6z/cfh/uNou720f/+xN28vPKf2wBjlcXUh9Crn5+eO/xplWcSGiSzOvSCLiQsrMXFhFlsur8S2n6xHrZ6d+X8k4IwSiMFxCMTWrrn2fEzPzXUes3Fkbracn5+NI7OL5ufLfuX8Qtvs/Hy0izzaedGZlo+86Fvurl+2fWHbvCgWtefRLpbZrm/MvCj7rLZDdSXLIsvKXwS7vw5mWRZZZ6mz6hjLS7ddfjlb2PYYfU51n1l0zpWLLIo8IopuMJFFURSdaUTk3XPqztbFwthFUbYWEZEVWRQRsbm9ObY/flNv770a+qfZ4HIlIiqdwLISC8FrpTPNIitf994z6JTSfWsUA5Ol7YveQkVfx5XXDY65eKxjbbfSNv1jFnmsKlkWkVUiKpXOtFq2lctZVKoL67rtlWpnXWXRtr1pdkLj9catRNTrWdRHI+ojWdTHOtPRiPpoFvWRznQ0en1GRvvbyj6VqhO1iPKjTfn0TMwfno724aPRPjwd7SOdaWd5vrfcaTtSTvOjs1G021G08/Ix3y6X8+58Xi7PL+0T7Tzy+XJa5Iv7zPf1HRzjRMK7MyLLFkKxhYNk70CaLRxQB9sWDtC99dmStoXj+ED/3jixZJzBsXtFRlGUx9yFRxZ5J0wu15XH7siyznEo6w2w8MpmnfaIdlaPb157e6fwzrrOcbbo/h+RLYzRrTurlD+03WlUOm1ZOd87SPTtuIhjH5eLvg5L2heN07/9ssfhZY7dy+17iG+3zrFz4TjaPS5Wu+3VRcfY7nKlf9vB42h1cd++fdRGshgZixgZ65+Wx8/BtoXp6HgWtZG+9zSw7vjIJLBu1au1qFdrcc7Y+LBLed6KxWFa0QnL8sXtfUFafuyQbbB9cSg3GOzlA/3LMcq6OvVFMTDfrXlxn+WXO9O+59pdXpg/ybGW6ZcXRUw9mce+Z/PY+2weh/YVMTfbFwZlg2cG1ZEi6iPlL8O1kYUwI7IiuudHC8tZuX3nPGg06tGo7488IooijyKKyIvy0Z0vX7Mi8s7yXHs+js7PxfT8XBydm4uj7UV/us7KZ1PJsmjU67Fl0+a4dPO5cfm558UV5zW753Jl16XnkQPtscz6xb/wL+l7CmMOrF+mbyWLyKqDYVJ/MFRZFDR1T27KUCkbONlZHEINjNcXRFUqWWTV5fblpGc9yrIsqo3yY5FxwbCrOb4izxdCspVCs3Z7IWw7oaBu+dCuN5+XIV1/YlP0pyUD4fnSNKU/XF+clveHPCuOXSw6fucLx+p9z+RxaG8RczPLJzpZFlGrR9RGiqjXy9CjWi16eVsW3fmid8zub6tFJa5oTpX3n+ulbOVlRUX5H0c3bSvb8iKK+fnIj8xEfnQ28umjkc/MDjy/bn3le28kRi86P8YvvTDGWhfFhhddMhguRsRA3jbQvnA8Wu54PHj8XrR+8fjLjFOGUtmiQKo7ny0JlHrt1eXbFwKpvjBrcf+qYy2wugjEANaALMuiVq1GLdzn5lRMHyrirvccicPfyeOi87JovbQal1xfiQ2bs2hszGJ8UxaNTVmMbywfYxvKX+SHab7djv1Hp2PP9OHYO32k8zgce44ciacP7o+vP/lgfPaJXbH3B0fiZzZeE/e97dedZMAal3WvOqqn+Sv63GwRH333kfjeY3lsnMii9cpqXPHiSmycKI/NjU3l8Xp8Y3nsHtsw/Kshi3Y75vYfjrk9B2Ju34GY23sw5vcejNm9B2L2mb2x/x+/HPvu+6eYm9oXG398a/zoF/84Kon++wKsNo7GAKx7U0+2I/KIm/+nsbj6x2prIjiqVatx/oaNcf6GjSv2yfM8/uf/+/745P/7lXju8KFobtx0FisEOL327s5jfjbije8Zi5dtXxvH6qxajZHzzomR885ZsU9RFPG9P/3f44k/+es4+uQz0bjiBWexQgBW4g6aAKx7M9MRT383jy1XVNfECdaJqlQq8ZILtsSDT30/5lfbDbkATtLcTMRTj+ex5YrKujpWZ1kWG6++Mg4+/GjkR2eHXQ4AHQIxAAAAAJIiEAMAAAAgKQIxAAAAAJIiEAMAAAAgKQIxAAAAAJIiEAMAAAAgKQIxAAAAAJIiEAMAAAAgKQIxAAAAAJIiEAMAAAAgKQIxAAAAAJIiEAMAAAAgKQIxAAAAAJIiEAMAAAAgKQIxAAAAAJIiEAMAAAAgKQIxAFgHpg4fiqIohl0GwCnJ8yIO7cuHXQYACakNuwAA4NT96GVXxMsvuzKuvfP9MVarx6Wbz41LN0/EJeecG+eON6IxMhKNeufRmd8wMrqkbXD9SNSrfkUAjq0oimjPRczORMzNFOX0aNFbnpuJmD3a395pmyli7mjEkYNF7J8qYv9UHvufK8e66sZqnP8Cf7MH4Mzz2y4ArGEXbjwnPv3f3xJf+97j8f39++L7B/bGk/v3xRP79sTDTz8ZR+Zmy8dsOZ1tz5/QuLVKJUaqtahWKlHNKlGrltNyOSunlZXbaovXV7K+vsu39bY5xriL22oD7Se2ryW1ZZ31y4xVybLIsogssoiIyLLOtPM6ZVk2MF+u6/aN3nL//OJxevPL9Fk69tJ9Ld2uf78rbHcifTrzeV5E5Fnk83lUa9WoRiWybDCw6F6c2LtIsYjoXa94zHXF4Lq+5v7+fZNy+2OsWzzeydVWHHOs/tqKIqLII/J2RJ5HFHnRmUa02/3rinK+sy7vtJfzC9t0xynXle392xTd9X3jLew7Im8Xg/3z5cdb2HdnrN64/dsWi8bu71P0xmnPlW0nolaPqI9GjIxlUR+NqI9mMb4xi4kLs7ji6npsbmax+YIsLn9pNcY3ZMcfEACeJ4EYAKxxrYnzozVx/gn1nW+3Y3p+Lo7Mzsbh2ZklgVn//Mz8fLSLPObzdrTzItp5Hu0i75sWC8uL2ubb7WgXi7bp6zM3P7doXVHupzvGCe6rrC1fsq/cx0dPu5ueeUtc/uT1wy5j3ckqEZVKRKVaTrvLWSVb0tZrr0Rk1Yhqtb9/RKW7bvF41Syq9b799LarDI5f7d9XtrS26mB7bSRiZDQbCLr6l0fGyuCrPlLWAACriUAMABJSq1ZjU7Uam0bHhl3KGVUUReSLA7mVQrSBPoNteVH07s1W9I3dXV6YH7zSqb+917a4T5zi2J3lPC/ie//cjicm2/HM9/PY90y+ME5nrlKNqI9FjI5nMTJWxMh41lmOqI9kEdUiKllEVIryyrCsiEolIrKILCsi685XIs754U0RM9+LPM+jHUUUndcnL4rIo4i8KF+/vNPeLtpxeK4MXg/NHI1Ds7NxeLacHpw9GnmRD9ZayeLKc8+Pqy7cEv/y8h+KH95ySe9Ktu7la1lfprJkXSztmy3KYPq3Oea6RYNmy+2/skxYVc2WDbcqlawTJi0OvRauyAMAzi6BGACw7mRZ1vto5Xr1wGdm4zt/OhMXtSpx4zXVaP1sNc6/uPwYWmNjFo1zyit1VmPgUhRFHJqdib3Th2PPkSOxd/pwPDK1O/7rd3fFfd/9+/hfv/Kf4xNveFu8Zdt/M+xSAYB1SiAGALAG7Xp4Pl768mq87f3jqzL0OpYsy2LT6FhsGh3rfdz3VS96Sfzbl/+ryPM8XvHRD8WXvvPPAjEA4IxZv382BQBYx9rz5cce11oYdjyVzpcezLbbwy4FAFjHBGIAAAAAJEUgBgAAAEBSBGIAAAAAJEUgBgAAAEBSBGIAAAAAJEUgBgAAAEBSBGIAAAAAJEUgBgAAAEBSBGIAAAAAJEUgBgAAAEBSBGIAAAAAJEUgBgAAAEBSBGIAAAAAJEUgBgAAAEBSBGIAwPO2b9++uPPOO2Pbtm1x6aWXxrZt2+LOO++Mffv2Dbs0AABYojbsAgCAte0b3/hGbN++PaampqIoioiIePLJJ+PBBx+MD37wg3H//ffH1q1bh1wlAAAscIUYAMl4alc75maLYZexruzbty+2b98ee/bs6YVhXUVRxJ49e2L79u2uFDuNiqKIvbvzOHLQexkA4FS5QgyAde+Kq6vRekkl7v29o1GpRFxwaSU2nZdFY1MW4xs7j00R4xuzaPSWsxhrZFGtRe9RqWZRq0dUquVylmXDfmpDd8899wxcGbZYu92OqampuPfee+PWW289y9WtLkVRRJ5H5PMR7fmI9nxRTtvl8txMEdOHijhysIjpQxHTh4qYPthpO9SZHiji2e/nMX2oHPPn/t3ocJ8UAMAaJRADYN2rjWTxbz/UiKe+k8cPdrVj9+N5HNpfBgx7due90GH6cESRn/i4lUpEtR5RrUZUap3wrNoN0LKodJfrEdVq1gnVFtb3wrbqwnK5TdYbtxvE9Qdz1VoZxGVZRGTlNIuF+cXruha3ZVm2Yv/ect+4y/X92H+8d8UwrKsoivhPf3JP/OtX/UpEEdHtXnTni4giYsm6WKatu6/edv39l1teZl2el2FUPh8xPx+Rzxe9UKobVOWLltvz0dfWH2QVvX75fES7XfRtF5EvWj4ZI2PRC2e74e1FrSyu/rFavOCF1bj4ykpMXOhifwCAUyEQAyAJI2NZXP7Salz+0uqKffK8iJnp6AVkR4+sEIR0go75ub517SLac2VIki/quyQ4aRcxN1PE0cML2+bziwKYzjiLA5u8fRZftBPwxHd2n3C/P/kfp89wNScuy/qv/FsUUC65GjDrCzYjqvUsRsb6g84y/Kz1B5p94WUZmHbX9wWnne27wWl9tAy9GpsixjZkUau7AhEA4EwRiAFAR6WSxfiGiPENqzeIKIoyWFvpqqqVrrhaaCuO3/8kxv6711wUD//TU8et+8ofuih+83/bcPyrzvrWRXfdiv2zJf1PZOxKpQy8AABIl0AMANaQLCuvXHoeI5yuUiIi4u3/w1vj3//7h475scksy+Id/+bmuOBSH+8DAGB18JspAHDKbr755mg2m1GtLv9R1Gq1Gs1mM9761ree5coAAGBlAjEA4JRNTEzE/fffH+edd96Sb93MsizOO++8uP/++2NiYmI4BQIAwDIEYgDA87J169Z45JFH4o477ogXXX5dbBq/OG644Ya444474pFHHomtW7cOu0QAABjgHmIAwPM2MTERt956a1zd/HfxXz83F7f/xcZhlwQAACtyhRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJAUgRgAAAAASRGIAQAAAJCUrCiKYthFwGqW7/9GRHU0KhuvGnYpAKve7u+247mnirj6x2rDLoU17O8f/VZsHB2NH73symGXAqfF7NS+eO6BndH8qZdH/ZwNwy4HgBCIAQAAAJAYH5kEAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICkCMQAAAACSIhADAAAAICn/P7aFRfoe53mPAAAAAElFTkSuQmCC"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 36, 
+ The orders were ringing out sharply now . The midshipmen were being marched in to dinner . Hardly a word did Dalzell speak as he ate .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> sample -&gt; 37, 
+ To night I hope replied Darrin . Oh then I must get busy . Why I 'm to represent you Darry .
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+<pre>
+<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
+<span class="ansi-red-fg">KeyboardInterrupt</span>                         Traceback (most recent call last)
+Cell <span class="ansi-green-fg">In[2], line 9</span>
+<span class="ansi-green-intense-fg ansi-bold">      7</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
+<span class="ansi-green-intense-fg ansi-bold">      8</span>     <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)"> sample -&gt; </span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>i<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">, </span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span style="color: rgb(175,0,0)"> </span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>line<span style="color: rgb(98,98,98)">.</span>strip()<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">'</span>)
+<span class="ansi-green-fg">----&gt; 9</span>     diagram <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">reader</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">text2circuit</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">line</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">strip</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">     10</span>     diagram<span style="color: rgb(98,98,98)">.</span>draw(figsize<span style="color: rgb(98,98,98)">=</span>(<span style="color: rgb(98,98,98)">12</span>, <span style="color: rgb(98,98,98)">8</span>), foliated<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>, draw_type_labels<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>, color_boxes<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>, color_wire<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>)
+<span class="ansi-green-intense-fg ansi-bold">     11</span> <span class="ansi-bold" style="color: rgb(0,135,0)">except</span> <span class="ansi-bold" style="color: rgb(215,95,95)">Exception</span> <span class="ansi-bold" style="color: rgb(0,135,0)">as</span> e:
+<span class="ansi-green-intense-fg ansi-bold">     12</span>     <span style="color: rgb(95,135,135)"># Skip lines that cause exceptions and print an error message</span>
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/lambeq-discocirc/lambeq_discocirc/reader.py:297</span>, in <span class="ansi-cyan-fg">DisCoCircReader.text2circuit</span><span class="ansi-blue-fg">(self, text, sandwich, break_cycles, pruned_nouns, rewrite_rules, foliated_frame_labels)</span>
+<span class="ansi-green-intense-fg ansi-bold">    294</span> noun_boxes <span style="color: rgb(98,98,98)">=</span> []
+<span class="ansi-green-intense-fg ansi-bold">    296</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> i, sentence <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span style="color: rgb(0,135,0)">enumerate</span>(sentences):
+<span class="ansi-green-fg">--&gt; 297</span>     tree <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_sentence2tree</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentence</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">break_cycles</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    299</span>     tree_toks <span style="color: rgb(98,98,98)">=</span> tree<span style="color: rgb(98,98,98)">.</span>get_words()
+<span class="ansi-green-intense-fg ansi-bold">    300</span>     reidxr <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_calculate_reindices(sentence, tree_toks)
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/lambeq-discocirc/lambeq_discocirc/reader.py:68</span>, in <span class="ansi-cyan-fg">DisCoCircReader._sentence2tree</span><span class="ansi-blue-fg">(self, sentence, break_cycles)</span>
+<span class="ansi-green-intense-fg ansi-bold">     64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_sentence2tree</span>(<span style="color: rgb(0,135,0)">self</span>, sentence: <span style="color: rgb(0,135,0)">str</span>, break_cycles: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">True</span>):
+<span class="ansi-green-intense-fg ansi-bold">     65</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Get the pregroup tree from a pregroup diagram generated by</span>
+<span class="ansi-green-intense-fg ansi-bold">     66</span> <span style="color: rgb(175,0,0)">    the CCG parser."""</span>
+<span class="ansi-green-fg">---&gt; 68</span>     d <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">ccg_parser</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">sentence2diagram</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentence</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">     70</span>     root <span style="color: rgb(98,98,98)">=</span> d<span style="color: rgb(98,98,98)">.</span>to_pregroup_tree(break_cycles<span style="color: rgb(98,98,98)">=</span>break_cycles)
+<span class="ansi-green-intense-fg ansi-bold">     72</span>     <span style="color: rgb(95,135,135)"># If diagram returns free wires, corrects by adding missing noun</span>
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/text2diagram/ccg_parser.py:232</span>, in <span class="ansi-cyan-fg">CCGParser.sentence2diagram</span><span class="ansi-blue-fg">(self, sentence, tokenised, planar, collapse_noun_phrases, suppress_exceptions)</span>
+<span class="ansi-green-intense-fg ansi-bold">    228</span>         <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">ValueError</span>(<span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">`tokenised` set to `True`, but variable </span><span style="color: rgb(175,0,0)">'</span>
+<span class="ansi-green-intense-fg ansi-bold">    229</span>                          <span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">`sentence` does not have type </span><span style="color: rgb(175,0,0)">'</span>
+<span class="ansi-green-intense-fg ansi-bold">    230</span>                          <span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">`list[str]`.</span><span style="color: rgb(175,0,0)">'</span>)
+<span class="ansi-green-intense-fg ansi-bold">    231</span>     sent: <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">str</span>] <span style="color: rgb(98,98,98)">=</span> [<span style="color: rgb(0,135,0)">str</span>(token) <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> token <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> sentence]
+<span class="ansi-green-fg">--&gt; 232</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">sentences2diagrams</span><span class="ansi-yellow-bg">(</span>
+<span class="ansi-green-intense-fg ansi-bold">    233</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg">sent</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    234</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">planar</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">planar</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    235</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">collapse_noun_phrases</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">collapse_noun_phrases</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    236</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    237</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    238</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">VerbosityLevel</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">SUPPRESS</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">value</span><span class="ansi-yellow-bg">)</span>[<span style="color: rgb(98,98,98)">0</span>]
+<span class="ansi-green-intense-fg ansi-bold">    239</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-intense-fg ansi-bold">    240</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span style="color: rgb(0,135,0)">isinstance</span>(sentence, <span style="color: rgb(0,135,0)">str</span>):
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/text2diagram/ccg_parser.py:163</span>, in <span class="ansi-cyan-fg">CCGParser.sentences2diagrams</span><span class="ansi-blue-fg">(self, sentences, tokenised, planar, collapse_noun_phrases, suppress_exceptions, verbose)</span>
+<span class="ansi-green-intense-fg ansi-bold">    123</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">sentences2diagrams</span>(<span style="color: rgb(0,135,0)">self</span>,
+<span class="ansi-green-intense-fg ansi-bold">    124</span>                        sentences: SentenceBatchType,
+<span class="ansi-green-intense-fg ansi-bold">    125</span>                        tokenised: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
+<span class="ansi-green-fg">   (...)</span>
+<span class="ansi-green-intense-fg ansi-bold">    128</span>                        suppress_exceptions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
+<span class="ansi-green-intense-fg ansi-bold">    129</span>                        verbose: <span style="color: rgb(0,135,0)">str</span> <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[Diagram <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>]:
+<span class="ansi-green-intense-fg ansi-bold">    130</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Parse multiple sentences into a list of lambeq diagrams.</span>
+<span class="ansi-green-intense-fg ansi-bold">    131</span> 
+<span class="ansi-green-intense-fg ansi-bold">    132</span> <span style="color: rgb(175,0,0)">    Parameters</span>
+<span class="ansi-green-fg">   (...)</span>
+<span class="ansi-green-intense-fg ansi-bold">    161</span> 
+<span class="ansi-green-intense-fg ansi-bold">    162</span> <span style="color: rgb(175,0,0)">    """</span>
+<span class="ansi-green-fg">--&gt; 163</span>     trees <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">sentences2trees</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentences</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    164</span> <span class="ansi-yellow-bg">                                 </span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    165</span> <span class="ansi-yellow-bg">                                 </span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    166</span> <span class="ansi-yellow-bg">                                 </span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    167</span>     diagrams: <span style="color: rgb(0,135,0)">list</span>[Diagram <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>] <span style="color: rgb(98,98,98)">=</span> []
+<span class="ansi-green-intense-fg ansi-bold">    168</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> verbose <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/text2diagram/bobcat_parser.py:280</span>, in <span class="ansi-cyan-fg">BobcatParser.sentences2trees</span><span class="ansi-blue-fg">(self, sentences, tokenised, suppress_exceptions, verbose)</span>
+<span class="ansi-green-intense-fg ansi-bold">    278</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> verbose <span style="color: rgb(98,98,98)">==</span> VerbosityLevel<span style="color: rgb(98,98,98)">.</span>TEXT<span style="color: rgb(98,98,98)">.</span>value:
+<span class="ansi-green-intense-fg ansi-bold">    279</span>     <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">Tagging sentences.</span><span style="color: rgb(175,0,0)">'</span>, file<span style="color: rgb(98,98,98)">=</span>sys<span style="color: rgb(98,98,98)">.</span>stderr)
+<span class="ansi-green-fg">--&gt; 280</span> tag_results <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">tagger</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentences</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    281</span> tags <span style="color: rgb(98,98,98)">=</span> tag_results<span style="color: rgb(98,98,98)">.</span>tags
+<span class="ansi-green-intense-fg ansi-bold">    282</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> verbose <span style="color: rgb(98,98,98)">==</span> VerbosityLevel<span style="color: rgb(98,98,98)">.</span>TEXT<span style="color: rgb(98,98,98)">.</span>value:
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/bobcat/tagger.py:402</span>, in <span class="ansi-cyan-fg">Tagger.__call__</span><span class="ansi-blue-fg">(self, inputs, batch_size, verbose)</span>
+<span class="ansi-green-intense-fg ansi-bold">    394</span> output <span style="color: rgb(98,98,98)">=</span> TaggerOutput(tags<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>model<span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>tags,
+<span class="ansi-green-intense-fg ansi-bold">    395</span>                       cats<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>model<span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>cats,
+<span class="ansi-green-intense-fg ansi-bold">    396</span>                       sentences<span style="color: rgb(98,98,98)">=</span>[])
+<span class="ansi-green-intense-fg ansi-bold">    398</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> i <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> trange(<span style="color: rgb(98,98,98)">0</span>, <span style="color: rgb(0,135,0)">len</span>(inputs), batch_size,
+<span class="ansi-green-intense-fg ansi-bold">    399</span>                 desc<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">Tagging sentences</span><span style="color: rgb(175,0,0)">'</span>,
+<span class="ansi-green-intense-fg ansi-bold">    400</span>                 leave<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
+<span class="ansi-green-intense-fg ansi-bold">    401</span>                 disable<span style="color: rgb(98,98,98)">=</span>verbose <span style="color: rgb(98,98,98)">!=</span> VerbosityLevel<span style="color: rgb(98,98,98)">.</span>PROGRESS<span style="color: rgb(98,98,98)">.</span>value):
+<span class="ansi-green-fg">--&gt; 402</span>     output<span style="color: rgb(98,98,98)">.</span>sentences<span style="color: rgb(98,98,98)">.</span>extend(<span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">parse</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">inputs</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg">i</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg">i</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">+</span><span class="ansi-yellow-bg">batch_size</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">)</span>)
+<span class="ansi-green-intense-fg ansi-bold">    404</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> output
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/bobcat/tagger.py:333</span>, in <span class="ansi-cyan-fg">Tagger.parse</span><span class="ansi-blue-fg">(self, inputs)</span>
+<span class="ansi-green-intense-fg ansi-bold">    331</span> <span style="color: rgb(175,0,0)">"""Parse a batch of sentences."""</span>
+<span class="ansi-green-intense-fg ansi-bold">    332</span> encodings <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>prepare_inputs(inputs, word_mask<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>)
+<span class="ansi-green-fg">--&gt; 333</span> outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">model</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">{</span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">torch</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">as_tensor</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">v</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">model</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    334</span> <span class="ansi-yellow-bg">                        </span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">for</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">v</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(175,0,255)">in</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">encodings</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">items</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">}</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    336</span> tag_output: <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">list</span>[TagListT]] <span style="color: rgb(98,98,98)">=</span> []
+<span class="ansi-green-intense-fg ansi-bold">    337</span> span_output: <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">list</span>[TagListT]] <span style="color: rgb(98,98,98)">=</span> []
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
+<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
+<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
+<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
+<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
+<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+
+File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/bobcat/tagger.py:132</span>, in <span class="ansi-cyan-fg">BertForChartClassification.forward</span><span class="ansi-blue-fg">(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, tag_labels, span_labels, word_mask, output_attentions, output_hidden_states, return_dict)</span>
+<span class="ansi-green-intense-fg ansi-bold">    114</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">forward</span>(
+<span class="ansi-green-intense-fg ansi-bold">    115</span>     <span style="color: rgb(0,135,0)">self</span>,
+<span class="ansi-green-intense-fg ansi-bold">    116</span>     input_ids: torch<span style="color: rgb(98,98,98)">.</span>LongTensor <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>,
+<span class="ansi-green-fg">   (...)</span>
+<span class="ansi-green-intense-fg ansi-bold">    127</span>     return_dict: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+<span class="ansi-green-intense-fg ansi-bold">    128</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ChartClassifierOutput <span style="color: rgb(98,98,98)">|</span> <span style="color: rgb(0,135,0)">tuple</span>[Any, <span style="color: rgb(98,98,98)">.</span><span style="color: rgb(98,98,98)">.</span><span style="color: rgb(98,98,98)">.</span>]:
+<span class="ansi-green-intense-fg ansi-bold">    129</span>     return_dict <span style="color: rgb(98,98,98)">=</span> (return_dict <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> return_dict <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+<span class="ansi-green-intense-fg ansi-bold">    130</span>                    <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>use_return_dict)
+<span class="ansi-green-fg">--&gt; 132</span>     outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">bert</span><span class="ansi-yellow-bg">(</span>
+<span class="ansi-green-intense-fg ansi-bold">    133</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">input_ids</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    134</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    135</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">token_type_ids</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">token_type_ids</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    136</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">position_ids</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">position_ids</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    137</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    138</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">inputs_embeds</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">inputs_embeds</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    139</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    140</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    141</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    142</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    144</span>     sequence_output <span style="color: rgb(98,98,98)">=</span> outputs[<span style="color: rgb(98,98,98)">0</span>]
+<span class="ansi-green-intense-fg ansi-bold">    145</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> word_mask <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
+<span class="ansi-green-intense-fg ansi-bold">    146</span>         <span style="color: rgb(95,135,135)"># remove ignored tensors and pack remaining ones</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
+<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
+<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
+<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
+<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
+<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:1021</span>, in <span class="ansi-cyan-fg">BertModel.forward</span><span class="ansi-blue-fg">(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1012</span> head_mask <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>get_head_mask(head_mask, <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>num_hidden_layers)
+<span class="ansi-green-intense-fg ansi-bold">   1014</span> embedding_output <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>embeddings(
+<span class="ansi-green-intense-fg ansi-bold">   1015</span>     input_ids<span style="color: rgb(98,98,98)">=</span>input_ids,
+<span class="ansi-green-intense-fg ansi-bold">   1016</span>     position_ids<span style="color: rgb(98,98,98)">=</span>position_ids,
+<span class="ansi-green-fg">   (...)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1019</span>     past_key_values_length<span style="color: rgb(98,98,98)">=</span>past_key_values_length,
+<span class="ansi-green-intense-fg ansi-bold">   1020</span> )
+<span class="ansi-green-fg">-&gt; 1021</span> encoder_outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">encoder</span><span class="ansi-yellow-bg">(</span>
+<span class="ansi-green-intense-fg ansi-bold">   1022</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">embedding_output</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1023</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">extended_attention_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1024</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1025</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoder_hidden_states</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">encoder_hidden_states</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1026</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoder_attention_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">encoder_extended_attention_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1027</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">past_key_values</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">past_key_values</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1028</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">use_cache</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">use_cache</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1029</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1030</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1031</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">   1032</span> <span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1033</span> sequence_output <span style="color: rgb(98,98,98)">=</span> encoder_outputs[<span style="color: rgb(98,98,98)">0</span>]
+<span class="ansi-green-intense-fg ansi-bold">   1034</span> pooled_output <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>pooler(sequence_output) <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>pooler <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
+<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
+<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
+<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
+<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
+<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:610</span>, in <span class="ansi-cyan-fg">BertEncoder.forward</span><span class="ansi-blue-fg">(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)</span>
+<span class="ansi-green-intense-fg ansi-bold">    601</span>     layer_outputs <span style="color: rgb(98,98,98)">=</span> torch<span style="color: rgb(98,98,98)">.</span>utils<span style="color: rgb(98,98,98)">.</span>checkpoint<span style="color: rgb(98,98,98)">.</span>checkpoint(
+<span class="ansi-green-intense-fg ansi-bold">    602</span>         create_custom_forward(layer_module),
+<span class="ansi-green-intense-fg ansi-bold">    603</span>         hidden_states,
+<span class="ansi-green-fg">   (...)</span>
+<span class="ansi-green-intense-fg ansi-bold">    607</span>         encoder_attention_mask,
+<span class="ansi-green-intense-fg ansi-bold">    608</span>     )
+<span class="ansi-green-intense-fg ansi-bold">    609</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-fg">--&gt; 610</span>     layer_outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">layer_module</span><span class="ansi-yellow-bg">(</span>
+<span class="ansi-green-intense-fg ansi-bold">    611</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">hidden_states</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    612</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    613</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">layer_head_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    614</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">encoder_hidden_states</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    615</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">encoder_attention_mask</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    616</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">past_key_value</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    617</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg">,</span>
+<span class="ansi-green-intense-fg ansi-bold">    618</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    620</span> hidden_states <span style="color: rgb(98,98,98)">=</span> layer_outputs[<span style="color: rgb(98,98,98)">0</span>]
+<span class="ansi-green-intense-fg ansi-bold">    621</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> use_cache:
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
+<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
+<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
+<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
+<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
+<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:538</span>, in <span class="ansi-cyan-fg">BertLayer.forward</span><span class="ansi-blue-fg">(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)</span>
+<span class="ansi-green-intense-fg ansi-bold">    535</span>     cross_attn_present_key_value <span style="color: rgb(98,98,98)">=</span> cross_attention_outputs[<span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">1</span>]
+<span class="ansi-green-intense-fg ansi-bold">    536</span>     present_key_value <span style="color: rgb(98,98,98)">=</span> present_key_value <span style="color: rgb(98,98,98)">+</span> cross_attn_present_key_value
+<span class="ansi-green-fg">--&gt; 538</span> layer_output <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">apply_chunking_to_forward</span><span class="ansi-yellow-bg">(</span>
+<span class="ansi-green-intense-fg ansi-bold">    539</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">feed_forward_chunk</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">chunk_size_feed_forward</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">seq_len_dim</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">attention_output</span>
+<span class="ansi-green-intense-fg ansi-bold">    540</span> <span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    541</span> outputs <span style="color: rgb(98,98,98)">=</span> (layer_output,) <span style="color: rgb(98,98,98)">+</span> outputs
+<span class="ansi-green-intense-fg ansi-bold">    543</span> <span style="color: rgb(95,135,135)"># if decoder, return the attn key/values as the last output</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/pytorch_utils.py:246</span>, in <span class="ansi-cyan-fg">apply_chunking_to_forward</span><span class="ansi-blue-fg">(forward_fn, chunk_size, chunk_dim, *input_tensors)</span>
+<span class="ansi-green-intense-fg ansi-bold">    243</span>     <span style="color: rgb(95,135,135)"># concatenate output at same dimension</span>
+<span class="ansi-green-intense-fg ansi-bold">    244</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> torch<span style="color: rgb(98,98,98)">.</span>cat(output_chunks, dim<span style="color: rgb(98,98,98)">=</span>chunk_dim)
+<span class="ansi-green-fg">--&gt; 246</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_fn</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">input_tensors</span><span class="ansi-yellow-bg">)</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:550</span>, in <span class="ansi-cyan-fg">BertLayer.feed_forward_chunk</span><span class="ansi-blue-fg">(self, attention_output)</span>
+<span class="ansi-green-intense-fg ansi-bold">    549</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">feed_forward_chunk</span>(<span style="color: rgb(0,135,0)">self</span>, attention_output):
+<span class="ansi-green-fg">--&gt; 550</span>     intermediate_output <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">intermediate</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">attention_output</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    551</span>     layer_output <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>output(intermediate_output, attention_output)
+<span class="ansi-green-intense-fg ansi-bold">    552</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> layer_output
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
+<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
+<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
+<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
+<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
+<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:450</span>, in <span class="ansi-cyan-fg">BertIntermediate.forward</span><span class="ansi-blue-fg">(self, hidden_states)</span>
+<span class="ansi-green-intense-fg ansi-bold">    449</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">forward</span>(<span style="color: rgb(0,135,0)">self</span>, hidden_states: torch<span style="color: rgb(98,98,98)">.</span>Tensor) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> torch<span style="color: rgb(98,98,98)">.</span>Tensor:
+<span class="ansi-green-fg">--&gt; 450</span>     hidden_states <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dense</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">hidden_states</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">    451</span>     hidden_states <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>intermediate_act_fn(hidden_states)
+<span class="ansi-green-intense-fg ansi-bold">    452</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> hidden_states
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
+<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
+<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
+<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
+<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
+<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
+<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
+<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
+
+File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/linear.py:116</span>, in <span class="ansi-cyan-fg">Linear.forward</span><span class="ansi-blue-fg">(self, input)</span>
+<span class="ansi-green-intense-fg ansi-bold">    115</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">forward</span>(<span style="color: rgb(0,135,0)">self</span>, <span style="color: rgb(0,135,0)">input</span>: Tensor) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> Tensor:
+<span class="ansi-green-fg">--&gt; 116</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">F</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">linear</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">input</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">weight</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">bias</span><span class="ansi-yellow-bg">)</span>
+
+<span class="ansi-red-fg">KeyboardInterrupt</span>: </pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+</main>
+</body>
+</html>

From d89ec0c727b2dcd4f6794ec40ebbcff3c6008c99 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 15:39:19 +0000
Subject: [PATCH 12/41] change signature to fix flake8 error

---
 lambeq/backend/drawing/drawable.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index fb518ffd..5e2ee534 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -850,7 +850,7 @@ def _add_box_with_nouns(
             off: int,
             x_pos: float,
             y_pos: float,
-            input_nouns: list[int] = []
+            input_nouns: Optional[list[int]]
     ) -> tuple[list[int], int, list[int]]:
         """Add a box to the graph, creating necessary wire endpoints.
 

From 8093b958ed6458704f262617246b26cb91456dca Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 11 Nov 2024 16:40:36 +0000
Subject: [PATCH 13/41] add doc strings to new functions

---
 lambeq/backend/drawing/drawable.py        | 13 ++++++++++---
 lambeq/backend/drawing/drawing_backend.py | 15 ++++++++++++++-
 2 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 5e2ee534..ac80b41a 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -856,11 +856,11 @@ def _add_box_with_nouns(
 
         Returns
         -------
-        list[int]
+        list : int
             The new scan of wire endpoints after adding the box
         box_ind : int
             The index of the newly added `BoxNode`
-        list[int]
+        input_nouns : list[int]
             The new order of input_nouns after adding the box
         """
         node = BoxNode(box, x_pos, y_pos)
@@ -1067,7 +1067,14 @@ def calculate_bounds(self) -> tuple[float, float, float, float]:
 
     @staticmethod
     def get_noun_id() -> int:
-        """Generate a new numerical ID for the noun box/wire."""
+        """Generate a new numerical ID for the noun box/wire.
+
+        Returns
+        -------
+        noun_id : int
+            returns a unique identifier for the noun wire/box
+        """
+
         # Increment and return the next available ID
         noun_id = DrawableDiagramWithFrames.noun_id_counter
         DrawableDiagramWithFrames.noun_id_counter += 1
diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index 04c97067..7863692a 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -195,8 +195,21 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
 
         """
 
-    def _get_wire_color(self, wire_id, **params):
+    def _get_wire_color(self, wire_id : int, **params) -> str:
         """
+         Retrieves a color that uniquely represent a given wire ID.
+
+        Parameters
+        ----------
+        wire_id : int
+            The noun identifier of the wire for which the color is
+            being retrieved.
+        **params:
+            Additional parameters.
+
+        Returns:
+        wire_color : str
+            The Hex color of the wire, represented as a string.
 
         """
         if not params.get('color_wires') or wire_id == 0:

From 69d983090c48c3cd70e67037a78da92b18286d5a Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Tue, 12 Nov 2024 15:48:37 +0000
Subject: [PATCH 14/41] make noun_id non-static variable

---
 lambeq/backend/drawing/drawable.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index ac80b41a..99fe9782 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -882,7 +882,7 @@ def _add_box_with_nouns(
             nbr_idx = scan[off + i]
             noun_id = (input_nouns[idx] if input_nouns
                        and idx < len(input_nouns)
-                       else DrawableDiagramWithFrames.get_noun_id()
+                       else self.get_noun_id()
                        )  # generate new noun id if needed
 
             wire_end = WireEndpoint(WireEndpointType.DOM,
@@ -908,7 +908,7 @@ def _add_box_with_nouns(
             if len(box.dom) == 1 and len(box.cod) > 1:
                 dom_noun = (input_nouns[off] if input_nouns
                             and off < len(input_nouns)
-                            else DrawableDiagramWithFrames.get_noun_id()
+                            else self.get_noun_id()
                             )
                 expanded_colors = [dom_noun] * len(box.cod)
                 input_nouns = (input_nouns[:off] + expanded_colors
@@ -916,7 +916,7 @@ def _add_box_with_nouns(
             elif len(box.dom) > 1 and len(box.cod) == 1:
                 cod_noun = (input_nouns[off] if input_nouns
                             and off < len(input_nouns)
-                            else DrawableDiagramWithFrames.get_noun_id()
+                            else self.get_noun_id()
                             )
                 input_nouns = (input_nouns[:off] + [cod_noun]
                                + input_nouns[off + len(box.dom):])
@@ -942,7 +942,7 @@ def _add_box_with_nouns(
             idx = off + i
             noun_id = (input_nouns[idx] if input_nouns
                        and idx < len(input_nouns)
-                       else DrawableDiagramWithFrames.get_noun_id())
+                       else self.get_noun_id())
             wire_end = WireEndpoint(WireEndpointType.COD,
                                     obj=obj,
                                     x=x,
@@ -1065,8 +1065,7 @@ def calculate_bounds(self) -> tuple[float, float, float, float]:
 
         return min(all_xs), min(all_ys), max(all_xs), max(all_ys)
 
-    @staticmethod
-    def get_noun_id() -> int:
+    def get_noun_id(self) -> int:
         """Generate a new numerical ID for the noun box/wire.
 
         Returns
@@ -1076,8 +1075,8 @@ def get_noun_id() -> int:
         """
 
         # Increment and return the next available ID
-        noun_id = DrawableDiagramWithFrames.noun_id_counter
-        DrawableDiagramWithFrames.noun_id_counter += 1
+        noun_id = self.noun_id_counter
+        self.noun_id_counter += 1
         return noun_id
 
     @classmethod

From 3af9164454df308949f1a3211efeef34cb08cbcc Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Tue, 12 Nov 2024 15:50:21 +0000
Subject: [PATCH 15/41] remove different colours and remove colours with
 similar shades

---
 lambeq/backend/drawing/drawing_backend.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index 7863692a..7cddeb90 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -31,15 +31,14 @@
 DEFAULT_ASPECT = 'equal'
 
 WIRE_COLORS = [
-    '#9c540e', '#d03b2d', '#066ee2', '#f4a940', '#271296',
-    '#f07e33', '#fe9100', '#574cfa', '#49a141', '#a629b3',
-    '#1d0b66', '#f8c84b', '#7446f2', '#007765', '#b60539',
-    '#751fa5', '#5ac358', '#c330b9', '#398889', '#3c8331',
-    '#740127', '#018c76', '#e2074c', '#0252a1', '#fea431',
-    '#a22417', '#450d06', '#d17800', '#3831a0', '#73b8fd'
+    '#9c540e', '#f4a940', '#066ee2', '#d03b2d', '#f0e68c',
+    '#574cfa', '#49a141', '#a629b3', '#271296', '#ff6347',
+    '#adff2f', '#7446f2', '#007765', '#b60539', '#ff00ff',
+    '#c330b9', '#73b8fd', '#ff1493', '#00bfff', '#ffb6c1',
+    '#740127', '#e2074c', '#0252a1', '#fea431', '#ffff00',
+    '#450d06', '#d17800', '#3831a0', '#ff4500', '#d8bfd8'
 ]
 
-
 FRAME_COLORS: list[str] = [
     '#fbe8e7', '#fee1ba', '#fff9e5', '#e8f8ea', '#dcfbf5',
     '#e2effe', '#dfdefe', '#f0e8fc', '#f8e6f6', '#ffd0df',

From 6bf61c8477d298ededb9aeb1d9f0f233cf422831 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Wed, 13 Nov 2024 13:42:08 +0000
Subject: [PATCH 16/41] remove yellow shades from colors list

---
 lambeq/backend/drawing/drawing_backend.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index 7cddeb90..8c9493bb 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -31,11 +31,11 @@
 DEFAULT_ASPECT = 'equal'
 
 WIRE_COLORS = [
-    '#9c540e', '#f4a940', '#066ee2', '#d03b2d', '#f0e68c',
+    '#9c540e', '#f4a940', '#066ee2', '#d03b2d', '#7fd68b',
     '#574cfa', '#49a141', '#a629b3', '#271296', '#ff6347',
     '#adff2f', '#7446f2', '#007765', '#b60539', '#ff00ff',
     '#c330b9', '#73b8fd', '#ff1493', '#00bfff', '#ffb6c1',
-    '#740127', '#e2074c', '#0252a1', '#fea431', '#ffff00',
+    '#740127', '#e2074c', '#0252a1', '#fea431', '#205356',
     '#450d06', '#d17800', '#3831a0', '#ff4500', '#d8bfd8'
 ]
 

From 222376afab8379d89e250b45d5453b15a9d3ae58 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Wed, 13 Nov 2024 14:32:36 +0000
Subject: [PATCH 17/41] remove optional for input_nouns

---
 lambeq/backend/drawing/drawable.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 99fe9782..513bab48 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -850,7 +850,7 @@ def _add_box_with_nouns(
             off: int,
             x_pos: float,
             y_pos: float,
-            input_nouns: Optional[list[int]]
+            input_nouns: list[int]
     ) -> tuple[list[int], int, list[int]]:
         """Add a box to the graph, creating necessary wire endpoints.
 

From d1a7100f485372674e678c0ebb84099af602c756 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Wed, 13 Nov 2024 15:33:44 +0000
Subject: [PATCH 18/41] different linewidth for box and wires, get from user
 using params

---
 lambeq/backend/drawing/drawing.py     |  3 ++-
 lambeq/backend/drawing/mat_backend.py | 10 ++++++----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index c4c27204..0c9ea014 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -128,7 +128,8 @@ def draw(diagram: Diagram, **params) -> None:
         backend = TikzBackend(
             use_tikzstyles=params.get('use_tikzstyles', None))
     else:
-        backend = MatBackend(figsize=params.get('figsize', None))
+        backend = MatBackend(figsize=params.get('figsize', None),
+                             wires_linewidth=params.get('wires_width', 1.25))
 
     min_size = 0.01
     max_v = max([v for point in ([point.coordinates for point in
diff --git a/lambeq/backend/drawing/mat_backend.py b/lambeq/backend/drawing/mat_backend.py
index 52062405..d2ab058f 100644
--- a/lambeq/backend/drawing/mat_backend.py
+++ b/lambeq/backend/drawing/mat_backend.py
@@ -37,10 +37,12 @@ class MatBackend(DrawingBackend):
     def __init__(self,
                  axis: plt.Axes | None = None,
                  figsize: tuple | None = None,
-                 linewidth: float = 1):
+                 box_linewidth: float = 1,
+                 wires_linewidth: float = 1.25):
         self.axis = axis or plt.subplots(figsize=figsize, facecolor='white')[1]
         self.default_aspect = 'equal' if figsize is None else 'auto'
-        self.linewidth = linewidth
+        self.box_linewidth = box_linewidth
+        self.wires_linewidth = wires_linewidth
         self.max_width: float = 0
 
     def draw_text(self, text: str, x: float, y: float, **params) -> None:
@@ -63,7 +65,7 @@ def draw_polygon(self, *points: list[float], color: str = 'white') -> None:
         codes += len(points[1:]) * [Path.LINETO] + [Path.CLOSEPOLY]
         path = Path(points + points[:1], codes)
         self.axis.add_patch(PathPatch(
-            path, facecolor=COLORS[color], linewidth=self.linewidth))
+            path, facecolor=COLORS[color], linewidth=self.box_linewidth))
 
     def draw_wire(self,
                   source: tuple[float, float],
@@ -110,7 +112,7 @@ def draw_wire(self,
                 ])
 
             self.axis.add_patch(PathPatch(path, facecolor='none',
-                                          linewidth=self.linewidth,
+                                          linewidth=self.wires_linewidth,
                                           edgecolor=color))
 
         self.max_width = max(self.max_width, source[0], target[0])

From 8da64d728f87af72a7fc9f66d0143c2edeca65f9 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 14 Nov 2024 21:00:03 +0000
Subject: [PATCH 19/41] remove invalid characters from stylenames which throw
 render errors

---
 lambeq/backend/drawing/tikz_backend.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 42cc4008..6417c0c5 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -104,7 +104,8 @@ def draw_polygon(self, *points: list[float], color: str = 'white') -> None:
         nodes.append(nodes[0])
 
         if self.use_tikzstyles:
-            style_name = 'box' if color == 'white' else f'{color}_box'
+            color_name = color.lstrip('#')
+            style_name = 'box' if color == 'white' else f'{color_name}_box'
             style = (f'\\tikzstyle{{{style_name}}}='
                      f'[-, fill={self.format_color(color)}]\n')
             if style not in self.edge_styles:

From 3c83b8337bc47902b8af0b6c73ed25726ff1b948 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 14 Nov 2024 21:02:07 +0000
Subject: [PATCH 20/41] color wire changes in Tikz_backend

---
 lambeq/backend/drawing/tikz_backend.py | 56 ++++++++++++++++++++++++--
 1 file changed, 52 insertions(+), 4 deletions(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 6417c0c5..470debe8 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -40,9 +40,39 @@ def __init__(self, use_tikzstyles: bool = False):
         self.edgelayer: list[str] = []
         self.max_width: float = 0
 
-    @staticmethod
-    def format_color(color: str) -> str:
+    def format_color(self, color: str) -> str:
+        """
+         Returns RGB value for the Hex.
+
+        Parameters
+        ----------
+        color : str
+            The Hex color code, represented as a string.
+
+        Returns
+        ----------
+        rgb-color: str
+            The RGB color format, represented as a string.
+
+        """
         hexcode = COLORS[color]
+        return self.get_rgb_for_hex(hexcode=hexcode)
+
+    def get_rgb_for_hex(self, hexcode: str) -> str:
+        """
+         Helper method to convert Hex to RGB
+
+        Parameters
+        ----------
+        hexcode : str
+            The Hex color, represented as a string.
+
+        Returns
+        ----------
+        rgb-color: str
+            The RGB color, represented as a string.
+
+        """
         rgb = [
             int(hex, 16) for hex in [hexcode[1:3], hexcode[3:5], hexcode[5:]]]
         return f'{{rgb,255: red,{rgb[0]}; green,{rgb[1]}; blue,{rgb[2]}}}'
@@ -127,7 +157,7 @@ def draw_wire(self,
                   color_id: int = 0,
                   **params) -> None:
 
-        # color = self._get_wire_color(color_id, **params)
+        color = self._get_wire_color(color_id, **params)
         out = (-90 if not bend_out or source[0] == target[0]
                else (180 if source[0] > target[0] else 0))
         inp = (90 if not bend_in or source[0] == target[0]
@@ -145,6 +175,24 @@ def draw_wire(self,
                 style = ''
             style += f'looseness={looseness}'
 
+        if self.use_tikzstyles:
+            # TikZ style for the wire color
+            color_name = color.lstrip('#')
+            wire_style_name = (f'{color_name}_wire'
+                               if color != '#000000' else 'black_wire')
+            wire_style = (f'\\tikzstyle{{{wire_style_name}}}='
+                          f'[-, draw={self.get_rgb_for_hex(color)}]\n')
+            if wire_style not in self.edge_styles:
+                self.edge_styles.append(wire_style)
+            wire_options = f'style={wire_style_name}'
+
+            # Concatenate additional styles like looseness if present
+            if style:
+                wire_options += f', {style}'
+
+        else:
+            wire_options = f'-, draw={{{color}}}'
+
         cmd = (
             '\\draw [in={}, out={}{}] '
             '({}.center) to ({}.center);\n')
@@ -156,7 +204,7 @@ def draw_wire(self,
 
         self.edgelayer.append(cmd.format(
             inp, out,
-            f', {style}' if style is not None else '',
+            f', {wire_options}' if wire_options is not None else '',
             self.nodes[source], self.nodes[target]))
 
     def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:

From e8c04f32d78dd5287dbe0b94a407d20c6392d1c2 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Fri, 15 Nov 2024 12:14:26 +0000
Subject: [PATCH 21/41] remove extra {}

---
 lambeq/backend/drawing/tikz_backend.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 470debe8..23a6ff64 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -191,7 +191,7 @@ def draw_wire(self,
                 wire_options += f', {style}'
 
         else:
-            wire_options = f'-, draw={{{color}}}'
+            wire_options = f'-, draw={self.get_rgb_for_hex(color)}'
 
         cmd = (
             '\\draw [in={}, out={}{}] '

From 7caef28707d8c8c86ff4e460a906b68259a01cc8 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Fri, 15 Nov 2024 22:17:54 +0000
Subject: [PATCH 22/41] static commands for tex file, new layer for labels

---
 lambeq/backend/drawing/tikz_backend.py | 45 +++++++++++++++++++-------
 1 file changed, 33 insertions(+), 12 deletions(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 23a6ff64..5f490c28 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -38,6 +38,7 @@ def __init__(self, use_tikzstyles: bool = False):
         self.nodes: dict[tuple[float, float], int] = {}
         self.nodelayer: list[str] = []
         self.edgelayer: list[str] = []
+        self.label_layer: list[str] = []
         self.max_width: float = 0
 
     def format_color(self, color: str) -> str:
@@ -80,15 +81,21 @@ def get_rgb_for_hex(self, hexcode: str) -> str:
     def add_node(self,
                  x: float,
                  y: float,
+                 is_label : bool = False,
                  text: str | None = None,
                  options: str | None = None) -> int:
         """ Add a node to the tikz picture, return its unique id. """
 
         node = max(self.nodes.values()) + 1 if self.nodes else 1
         text = '' if text is None else text
-
-        self.nodelayer.append(
-            f'\\node [{options or ""}] ({node}) at ({x}, {y}) {{{text}}};\n')
+        if is_label:
+            self.label_layer.append(
+                f'\\node [{options or ""}] ({node}) '
+                f'at ({x}, {y}) {{{text}}};\n')
+        else:
+            self.nodelayer.append(
+                f'\\node [{options or ""}] ({node}) '
+                f'at ({x}, {y}) {{{text}}};\n')
         self.nodes.update({(x, y): node})
 
         self.max_width = max(self.max_width, x)
@@ -107,14 +114,14 @@ def draw_node(self,
         if 'color' in params:
             options.append(params['color'])
 
-        self.add_node(x, y, text, options=', '.join(options))
+        self.add_node(x, y, text=text, options=', '.join(options))
 
     def draw_text(self,
                   text: str,
                   x: float,
                   y: float,
                   **params) -> None:
-        options = 'style=none, fill=white'
+        options = 'style=none'
 
         if params.get('horizontalalignment', 'center') == 'left':
             options += ', anchor=west'
@@ -123,7 +130,7 @@ def draw_text(self,
         if 'fontsize' in params and params['fontsize'] is not None:
             options += f', scale={params["fontsize"]}'
 
-        self.add_node(x, y, text, options)
+        self.add_node(x, y, text=text, options=options, is_label=True)
 
     def draw_polygon(self, *points: list[float], color: str = 'white') -> None:
         nodes: list[int] = []
@@ -142,7 +149,7 @@ def draw_polygon(self, *points: list[float], color: str = 'white') -> None:
                 self.edge_styles.append(style)
             options = f'style={style_name}'
         else:
-            options = f'-, fill={{{color}}}'
+            options = f'-, fill={{{self.format_color(color)}}}'
 
         str_connections = ' to '.join(f'({node}.center)' for node in nodes)
         self.edgelayer.append(f'\\draw [{options}] {str_connections};\n')
@@ -226,7 +233,7 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
                 if params.get('nodesize', 1) != 1:
                     options += f', scale={params.get("nodesize")}'
 
-                self.add_node(i, j, '', options)
+                self.add_node(i, j, is_label=False, text='', options=options)
 
             for wire in node.cod_wires:
                 self.draw_wire(node.coordinates,
@@ -254,8 +261,17 @@ def output(self, path=None, show=True, **params) -> None:
                  + self.nodelayer + ['\\end{pgfonlayer}\n'])
         edges = (['\\begin{pgfonlayer}{edgelayer}\n'] + self.edgelayer
                  + ['\\end{pgfonlayer}\n'])
+        labels = (['\\begin{pgfonlayer}{labellayer}\n'] + self.label_layer
+                  + ['\\end{pgfonlayer}\n'])
         end = ['\\end{tikzpicture}\n']
-
+        tex_comments = (
+            "% When embedding into a *.tex file, uncomment and include "
+            "the following lines:\n"
+            "% \\pgfdeclarelayer{nodelayer}\n"
+            "% \\pgfdeclarelayer{edgelayer}\n"
+            "% \\pgfdeclarelayer{labellayer}\n"
+            "% \\pgfsetlayers{nodelayer, edgelayer, labellayer}\n"
+        )
         if path is not None:
             if output_tikzstyle:
                 style_path = '.'.join(path.split('.')[:-1]) + '.tikzstyles'
@@ -263,9 +279,14 @@ def output(self, path=None, show=True, **params) -> None:
                     file.writelines(['% Node styles\n'] + self.node_styles)
                     file.writelines(['% Edge styles\n'] + self.edge_styles)
             with open(path, 'w+') as file:
-                file.writelines(begin + nodes + edges + end)
+                file.writelines([tex_comments] + begin + nodes + edges
+                                + labels + end)
 
         elif show:
+            tex_output = tex_comments
+
             if output_tikzstyle:
-                print(''.join(self.node_styles + self.edge_styles))
-            print(''.join(begin + nodes + edges + end))
+                tex_output += ''.join(self.node_styles + self.edge_styles)
+
+            tex_output += ''.join(begin + nodes + edges + labels + end)
+            print(tex_output)

From 3d9be557e63aaad73c39022809899bb39ddc7dff Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Fri, 15 Nov 2024 22:28:21 +0000
Subject: [PATCH 23/41] flake8 suggestion to remove quotes

---
 lambeq/backend/drawing/tikz_backend.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 5f490c28..ac93127f 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -265,12 +265,12 @@ def output(self, path=None, show=True, **params) -> None:
                   + ['\\end{pgfonlayer}\n'])
         end = ['\\end{tikzpicture}\n']
         tex_comments = (
-            "% When embedding into a *.tex file, uncomment and include "
-            "the following lines:\n"
-            "% \\pgfdeclarelayer{nodelayer}\n"
-            "% \\pgfdeclarelayer{edgelayer}\n"
-            "% \\pgfdeclarelayer{labellayer}\n"
-            "% \\pgfsetlayers{nodelayer, edgelayer, labellayer}\n"
+            '% When embedding into a *.tex file, uncomment and include '
+            'the following lines:\n'
+            '% \\pgfdeclarelayer{nodelayer}\n'
+            '% \\pgfdeclarelayer{edgelayer}\n'
+            '% \\pgfdeclarelayer{labellayer}\n'
+            '% \\pgfsetlayers{nodelayer, edgelayer, labellayer}\n'
         )
         if path is not None:
             if output_tikzstyle:

From 57fe2d9e582c1436432433838da2d6eedf198ec6 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 18 Nov 2024 16:41:21 +0000
Subject: [PATCH 24/41] remove extra {}

---
 lambeq/backend/drawing/tikz_backend.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index ac93127f..a25cb2cb 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -149,7 +149,7 @@ def draw_polygon(self, *points: list[float], color: str = 'white') -> None:
                 self.edge_styles.append(style)
             options = f'style={style_name}'
         else:
-            options = f'-, fill={{{self.format_color(color)}}}'
+            options = f'-, fill={self.format_color(color)}'
 
         str_connections = ' to '.join(f'({node}.center)' for node in nodes)
         self.edgelayer.append(f'\\draw [{options}] {str_connections};\n')

From edac6cd3ce7c8d3a6f11a74fc6d453dcbf2e4d3d Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Mon, 18 Nov 2024 16:42:46 +0000
Subject: [PATCH 25/41] delete sample file

---
 tests/backend/simple-books-frame-drawing.html | 8286 -----------------
 1 file changed, 8286 deletions(-)
 delete mode 100644 tests/backend/simple-books-frame-drawing.html

diff --git a/tests/backend/simple-books-frame-drawing.html b/tests/backend/simple-books-frame-drawing.html
deleted file mode 100644
index f2dc8e24..00000000
--- a/tests/backend/simple-books-frame-drawing.html
+++ /dev/null
@@ -1,8286 +0,0 @@
-<!DOCTYPE html>
-
-<html lang="en">
-<head><meta charset="utf-8"/>
-<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>simple-books-frame-drawing</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
-<style type="text/css">
-    pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-.highlight .hll { background-color: var(--jp-cell-editor-active-background) }
-.highlight { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
-.highlight .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
-.highlight .err { color: var(--jp-mirror-editor-error-color) } /* Error */
-.highlight .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
-.highlight .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
-.highlight .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
-.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
-.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
-.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
-.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
-.highlight .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
-.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
-.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
-.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
-.highlight .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
-.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
-.highlight .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
-.highlight .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
-.highlight .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
-.highlight .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
-.highlight .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
-.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
-.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
-.highlight .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
-.highlight .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
-.highlight .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
-.highlight .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
-.highlight .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
-.highlight .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
-.highlight .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
-.highlight .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
-.highlight .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
-.highlight .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
-.highlight .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
-.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
-  </style>
-<style type="text/css">
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*
- * Mozilla scrollbar styling
- */
-
-/* use standard opaque scrollbars for most nodes */
-[data-jp-theme-scrollbars='true'] {
-  scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
-    var(--jp-scrollbar-background-color);
-}
-
-/* for code nodes, use a transparent style of scrollbar. These selectors
- * will match lower in the tree, and so will override the above */
-[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
-[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
-  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
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-.jp-scrollbar-tiny {
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- * Lumino
- */
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-.lm-ScrollBar[data-orientation='horizontal'] {
-  min-height: 16px;
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- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
-| Distributed under the terms of the BSD 3-Clause License.
-|
-| The full license is in the file LICENSE, distributed with this software.
-|----------------------------------------------------------------------------*/
-
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-  position: relative;
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-.lm-Widget.lm-mod-hidden {
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-  display: block;
-  transform-origin: top left;
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- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
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-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
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-.lm-CommandPalette {
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- * Distributed under the terms of the Modified BSD License.
- */
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-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
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-| Distributed under the terms of the BSD 3-Clause License.
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- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
-| Distributed under the terms of the BSD 3-Clause License.
-|
-| The full license is in the file LICENSE, distributed with this software.
-|----------------------------------------------------------------------------*/
-
-.lm-MenuBar {
-  outline: none;
-  -webkit-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-}
-
-.lm-MenuBar-content {
-  margin: 0;
-  padding: 0;
-  display: flex;
-  flex-direction: row;
-  list-style-type: none;
-}
-
-.lm-MenuBar-item {
-  box-sizing: border-box;
-}
-
-.lm-MenuBar-itemIcon,
-.lm-MenuBar-itemLabel {
-  display: inline-block;
-}
-
-/*
- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
-| Distributed under the terms of the BSD 3-Clause License.
-|
-| The full license is in the file LICENSE, distributed with this software.
-|----------------------------------------------------------------------------*/
-
-.lm-ScrollBar {
-  display: flex;
-  -webkit-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-}
-
-.lm-ScrollBar[data-orientation='horizontal'] {
-  flex-direction: row;
-}
-
-.lm-ScrollBar[data-orientation='vertical'] {
-  flex-direction: column;
-}
-
-.lm-ScrollBar-button {
-  box-sizing: border-box;
-  flex: 0 0 auto;
-}
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-.lm-ScrollBar-track {
-  box-sizing: border-box;
-  position: relative;
-  overflow: hidden;
-  flex: 1 1 auto;
-}
-
-.lm-ScrollBar-thumb {
-  box-sizing: border-box;
-  position: absolute;
-}
-
-/*
- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
-| Distributed under the terms of the BSD 3-Clause License.
-|
-| The full license is in the file LICENSE, distributed with this software.
-|----------------------------------------------------------------------------*/
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-.lm-SplitPanel-child {
-  z-index: 0;
-}
-
-.lm-SplitPanel-handle {
-  z-index: 1;
-}
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-.lm-SplitPanel-handle.lm-mod-hidden {
-  display: none !important;
-}
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-.lm-SplitPanel-handle:after {
-  position: absolute;
-  top: 0;
-  left: 0;
-  width: 100%;
-  height: 100%;
-  content: '';
-}
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-.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
-  cursor: ew-resize;
-}
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-.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
-  cursor: ns-resize;
-}
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-.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
-  left: 50%;
-  min-width: 8px;
-  transform: translateX(-50%);
-}
-
-.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
-  top: 50%;
-  min-height: 8px;
-  transform: translateY(-50%);
-}
-
-/*
- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
-| Distributed under the terms of the BSD 3-Clause License.
-|
-| The full license is in the file LICENSE, distributed with this software.
-|----------------------------------------------------------------------------*/
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-.lm-TabBar {
-  display: flex;
-  -webkit-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
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-.lm-TabBar[data-orientation='horizontal'] {
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-  align-items: flex-end;
-}
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-.lm-TabBar[data-orientation='vertical'] {
-  flex-direction: column;
-  align-items: flex-end;
-}
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-.lm-TabBar-content {
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-  padding: 0;
-  display: flex;
-  flex: 1 1 auto;
-  list-style-type: none;
-}
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-.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
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-.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
-  flex-direction: column;
-}
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-.lm-TabBar-tab {
-  display: flex;
-  flex-direction: row;
-  box-sizing: border-box;
-  overflow: hidden;
-  touch-action: none; /* Disable native Drag/Drop */
-}
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-.lm-TabBar-tabIcon,
-.lm-TabBar-tabCloseIcon {
-  flex: 0 0 auto;
-}
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-.lm-TabBar-tabLabel {
-  flex: 1 1 auto;
-  overflow: hidden;
-  white-space: nowrap;
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-.lm-TabBar-tabInput {
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-  width: 100%;
-  box-sizing: border-box;
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-.lm-TabBar-tab.lm-mod-hidden {
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-.lm-TabBar-addButton.lm-mod-hidden {
-  display: none !important;
-}
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-.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
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-.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
-  left: 0;
-  transition: left 150ms ease;
-}
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-.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
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-  transition: top 150ms ease;
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-.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
-  transition: none;
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-.lm-TabBar-tabLabel .lm-TabBar-tabInput {
-  user-select: all;
-  width: 100%;
-  box-sizing: border-box;
-  background: inherit;
-}
-
-/*
- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
-| Distributed under the terms of the BSD 3-Clause License.
-|
-| The full license is in the file LICENSE, distributed with this software.
-|----------------------------------------------------------------------------*/
-
-.lm-TabPanel-tabBar {
-  z-index: 1;
-}
-
-.lm-TabPanel-stackedPanel {
-  z-index: 0;
-}
-
-/*
- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Copyright (c) 2014-2017, PhosphorJS Contributors
-|
-| Distributed under the terms of the BSD 3-Clause License.
-|
-| The full license is in the file LICENSE, distributed with this software.
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-.jp-Collapse {
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-
-.jp-Collapse-header {
-  padding: 1px 12px;
-  background-color: var(--jp-layout-color1);
-  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
-  color: var(--jp-ui-font-color1);
-  cursor: pointer;
-  display: flex;
-  align-items: center;
-  font-size: var(--jp-ui-font-size0);
-  font-weight: 600;
-  text-transform: uppercase;
-  user-select: none;
-}
-
-.jp-Collapser-icon {
-  height: 16px;
-}
-
-.jp-Collapse-header-collapsed .jp-Collapser-icon {
-  transform: rotate(-90deg);
-  margin: auto 0;
-}
-
-.jp-Collapser-title {
-  line-height: 25px;
-}
-
-.jp-Collapse-contents {
-  padding: 0 12px;
-  background-color: var(--jp-layout-color1);
-  color: var(--jp-ui-font-color1);
-  overflow: auto;
-}
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
-
-/**
- * (DEPRECATED) Support for consuming icons as CSS background images
- */
-
-/* Icons urls */
-
-:root {
-  --jp-icon-add-above: url(data:image/svg+xml;base64,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);
-  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
-  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
-  --jp-icon-bell: url(data:image/svg+xml;base64,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);
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-  --jp-icon-react: url(data:image/svg+xml;base64,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);
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-  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
-  --jp-icon-regex: url(data:image/svg+xml;base64,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);
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-  --jp-icon-toc: url(data:image/svg+xml;base64,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);
-  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
-  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
-  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-users: url(data:image/svg+xml;base64,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);
-  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-word: url(data:image/svg+xml;base64,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);
-  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
-}
-
-/* Icon CSS class declarations */
-
-.jp-AddAboveIcon {
-  background-image: var(--jp-icon-add-above);
-}
-
-.jp-AddBelowIcon {
-  background-image: var(--jp-icon-add-below);
-}
-
-.jp-AddIcon {
-  background-image: var(--jp-icon-add);
-}
-
-.jp-BellIcon {
-  background-image: var(--jp-icon-bell);
-}
-
-.jp-BugDotIcon {
-  background-image: var(--jp-icon-bug-dot);
-}
-
-.jp-BugIcon {
-  background-image: var(--jp-icon-bug);
-}
-
-.jp-BuildIcon {
-  background-image: var(--jp-icon-build);
-}
-
-.jp-CaretDownEmptyIcon {
-  background-image: var(--jp-icon-caret-down-empty);
-}
-
-.jp-CaretDownEmptyThinIcon {
-  background-image: var(--jp-icon-caret-down-empty-thin);
-}
-
-.jp-CaretDownIcon {
-  background-image: var(--jp-icon-caret-down);
-}
-
-.jp-CaretLeftIcon {
-  background-image: var(--jp-icon-caret-left);
-}
-
-.jp-CaretRightIcon {
-  background-image: var(--jp-icon-caret-right);
-}
-
-.jp-CaretUpEmptyThinIcon {
-  background-image: var(--jp-icon-caret-up-empty-thin);
-}
-
-.jp-CaretUpIcon {
-  background-image: var(--jp-icon-caret-up);
-}
-
-.jp-CaseSensitiveIcon {
-  background-image: var(--jp-icon-case-sensitive);
-}
-
-.jp-CheckIcon {
-  background-image: var(--jp-icon-check);
-}
-
-.jp-CircleEmptyIcon {
-  background-image: var(--jp-icon-circle-empty);
-}
-
-.jp-CircleIcon {
-  background-image: var(--jp-icon-circle);
-}
-
-.jp-ClearIcon {
-  background-image: var(--jp-icon-clear);
-}
-
-.jp-CloseIcon {
-  background-image: var(--jp-icon-close);
-}
-
-.jp-CodeCheckIcon {
-  background-image: var(--jp-icon-code-check);
-}
-
-.jp-CodeIcon {
-  background-image: var(--jp-icon-code);
-}
-
-.jp-CollapseAllIcon {
-  background-image: var(--jp-icon-collapse-all);
-}
-
-.jp-ConsoleIcon {
-  background-image: var(--jp-icon-console);
-}
-
-.jp-CopyIcon {
-  background-image: var(--jp-icon-copy);
-}
-
-.jp-CopyrightIcon {
-  background-image: var(--jp-icon-copyright);
-}
-
-.jp-CutIcon {
-  background-image: var(--jp-icon-cut);
-}
-
-.jp-DeleteIcon {
-  background-image: var(--jp-icon-delete);
-}
-
-.jp-DownloadIcon {
-  background-image: var(--jp-icon-download);
-}
-
-.jp-DuplicateIcon {
-  background-image: var(--jp-icon-duplicate);
-}
-
-.jp-EditIcon {
-  background-image: var(--jp-icon-edit);
-}
-
-.jp-EllipsesIcon {
-  background-image: var(--jp-icon-ellipses);
-}
-
-.jp-ErrorIcon {
-  background-image: var(--jp-icon-error);
-}
-
-.jp-ExpandAllIcon {
-  background-image: var(--jp-icon-expand-all);
-}
-
-.jp-ExtensionIcon {
-  background-image: var(--jp-icon-extension);
-}
-
-.jp-FastForwardIcon {
-  background-image: var(--jp-icon-fast-forward);
-}
-
-.jp-FileIcon {
-  background-image: var(--jp-icon-file);
-}
-
-.jp-FileUploadIcon {
-  background-image: var(--jp-icon-file-upload);
-}
-
-.jp-FilterDotIcon {
-  background-image: var(--jp-icon-filter-dot);
-}
-
-.jp-FilterIcon {
-  background-image: var(--jp-icon-filter);
-}
-
-.jp-FilterListIcon {
-  background-image: var(--jp-icon-filter-list);
-}
-
-.jp-FolderFavoriteIcon {
-  background-image: var(--jp-icon-folder-favorite);
-}
-
-.jp-FolderIcon {
-  background-image: var(--jp-icon-folder);
-}
-
-.jp-HomeIcon {
-  background-image: var(--jp-icon-home);
-}
-
-.jp-Html5Icon {
-  background-image: var(--jp-icon-html5);
-}
-
-.jp-ImageIcon {
-  background-image: var(--jp-icon-image);
-}
-
-.jp-InfoIcon {
-  background-image: var(--jp-icon-info);
-}
-
-.jp-InspectorIcon {
-  background-image: var(--jp-icon-inspector);
-}
-
-.jp-JsonIcon {
-  background-image: var(--jp-icon-json);
-}
-
-.jp-JuliaIcon {
-  background-image: var(--jp-icon-julia);
-}
-
-.jp-JupyterFaviconIcon {
-  background-image: var(--jp-icon-jupyter-favicon);
-}
-
-.jp-JupyterIcon {
-  background-image: var(--jp-icon-jupyter);
-}
-
-.jp-JupyterlabWordmarkIcon {
-  background-image: var(--jp-icon-jupyterlab-wordmark);
-}
-
-.jp-KernelIcon {
-  background-image: var(--jp-icon-kernel);
-}
-
-.jp-KeyboardIcon {
-  background-image: var(--jp-icon-keyboard);
-}
-
-.jp-LaunchIcon {
-  background-image: var(--jp-icon-launch);
-}
-
-.jp-LauncherIcon {
-  background-image: var(--jp-icon-launcher);
-}
-
-.jp-LineFormIcon {
-  background-image: var(--jp-icon-line-form);
-}
-
-.jp-LinkIcon {
-  background-image: var(--jp-icon-link);
-}
-
-.jp-ListIcon {
-  background-image: var(--jp-icon-list);
-}
-
-.jp-MarkdownIcon {
-  background-image: var(--jp-icon-markdown);
-}
-
-.jp-MoveDownIcon {
-  background-image: var(--jp-icon-move-down);
-}
-
-.jp-MoveUpIcon {
-  background-image: var(--jp-icon-move-up);
-}
-
-.jp-NewFolderIcon {
-  background-image: var(--jp-icon-new-folder);
-}
-
-.jp-NotTrustedIcon {
-  background-image: var(--jp-icon-not-trusted);
-}
-
-.jp-NotebookIcon {
-  background-image: var(--jp-icon-notebook);
-}
-
-.jp-NumberingIcon {
-  background-image: var(--jp-icon-numbering);
-}
-
-.jp-OfflineBoltIcon {
-  background-image: var(--jp-icon-offline-bolt);
-}
-
-.jp-PaletteIcon {
-  background-image: var(--jp-icon-palette);
-}
-
-.jp-PasteIcon {
-  background-image: var(--jp-icon-paste);
-}
-
-.jp-PdfIcon {
-  background-image: var(--jp-icon-pdf);
-}
-
-.jp-PythonIcon {
-  background-image: var(--jp-icon-python);
-}
-
-.jp-RKernelIcon {
-  background-image: var(--jp-icon-r-kernel);
-}
-
-.jp-ReactIcon {
-  background-image: var(--jp-icon-react);
-}
-
-.jp-RedoIcon {
-  background-image: var(--jp-icon-redo);
-}
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-/*-----------------------------------------------------------------------------
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-|
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-|----------------------------------------------------------------------------*/
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-/*-----------------------------------------------------------------------------
-| Copyright (c) 2014-2017, Jupyter Development Team.
-|
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-input.jp-mod-styled:focus {
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-/*-----------------------------------------------------------------------------
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-.jp-switch {
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-/*-----------------------------------------------------------------------------
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-|
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-:root {
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-/* Toolbar buttons */
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-/* This is the div we use to wrap the react component into a Widget */
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-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
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-/* Sibling imports */
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-/*-----------------------------------------------------------------------------
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-/* Make the output collapser disappear when there is not output, but do so
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-/*-----------------------------------------------------------------------------
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-/*-----------------------------------------------------------------------------
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-.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
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-.jp-showHiddenCellsButton {
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-
-/*-----------------------------------------------------------------------------
-| Printing
-|----------------------------------------------------------------------------*/
-
-/*
-Using block instead of flex to allow the use of the break-inside CSS property for
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-*/
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-@media print {
-  .jp-Cell-inputWrapper,
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-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
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-|----------------------------------------------------------------------------*/
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-:root {
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-
-/*-----------------------------------------------------------------------------
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-/*-----------------------------------------------------------------------------
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-|----------------------------------------------------------------------------*/
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-.jp-NotebookPanel-toolbar {
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-.jp-Toolbar-responsive-popup {
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-}
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-.jp-Toolbar > .jp-Toolbar-responsive-opener {
-  margin-left: auto;
-}
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| Variables
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-
-/*-----------------------------------------------------------------------------
-| Styles
-|----------------------------------------------------------------------------*/
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-.jp-Notebook-ExecutionIndicator {
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-.jp-Notebook-ExecutionIndicator span {
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-/*
- * Copyright (c) Jupyter Development Team.
- * Distributed under the terms of the Modified BSD License.
- */
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-/*
- * Execution indicator
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-.jp-tocItem-content::after {
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-  /* Must be identical to form a circle */
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-.jp-tocItem-content[data-running='0'],
-.jp-tocItem-content[data-running='1'] {
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-/*
- * Copyright (c) Jupyter Development Team.
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- */
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-.jp-Notebook-footer {
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-  );
-  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
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-.jp-Notebook-footer:focus {
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-}
-
-/* For devices that support hovering, hide footer until hover */
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-  .jp-Notebook-footer {
-    opacity: 0;
-  }
-
-  .jp-Notebook-footer:focus,
-  .jp-Notebook-footer:hover {
-    opacity: 1;
-  }
-}
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| Imports
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| CSS variables
-|----------------------------------------------------------------------------*/
-
-:root {
-  --jp-side-by-side-output-size: 1fr;
-  --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
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-  --jp-private-notebook-dragImage-height: 36px;
-  --jp-private-notebook-selected-color: var(--md-blue-400);
-  --jp-private-notebook-active-color: var(--md-green-400);
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-
-/*-----------------------------------------------------------------------------
-| Notebook
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-/* stylelint-disable selector-max-class */
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-.jp-NotebookPanel {
-  display: block;
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-.jp-NotebookPanel.jp-Document {
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-.jp-Notebook {
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-.jp-Notebook .jp-Cell {
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-.jp-Notebook .jp-Cell .jp-InputPrompt {
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-/*-----------------------------------------------------------------------------
-| Notebook state related styling
-|
-| The notebook and cells each have states, here are the possibilities:
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-|   - Command
-|   - Edit
-| - Cell
-|   - None
-|   - Active (only one can be active)
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-|   - Multiselected (when multiple selected, the cursor)
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-|----------------------------------------------------------------------------*/
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-/* Command or edit modes */
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-.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
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-.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
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-/* cell is active */
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-/* cell is dirty */
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-.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
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-.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
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-/* collapser is hovered */
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-/* cell is active and collapser is hovered */
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-/* Command mode */
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-.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
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-}
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-.jp-Notebook.jp-mod-commandMode
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-/* Edit mode */
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-.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
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-
-/*-----------------------------------------------------------------------------
-| Notebook drag and drop
-|----------------------------------------------------------------------------*/
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-.jp-Notebook-cell.jp-mod-dropSource {
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-.jp-Notebook-cell.jp-mod-dropTarget,
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-.jp-dragImage-singlePrompt {
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-.jp-dragImage .jp-dragImage-prompt {
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-  padding-left: 12px;
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-  line-height: 1.9;
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-.jp-dragImage-multipleBack {
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-  left: 8px;
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-  border: var(--jp-border-width) solid var(--jp-input-border-color);
-  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
-}
-
-/*-----------------------------------------------------------------------------
-| Cell toolbar
-|----------------------------------------------------------------------------*/
-
-.jp-NotebookTools {
-  display: block;
-  min-width: var(--jp-sidebar-min-width);
-  color: var(--jp-ui-font-color1);
-  background: var(--jp-layout-color1);
-
-  /* This is needed so that all font sizing of children done in ems is
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-.jp-KeySelector label,
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-.jp-NumberSetter input {
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-
-.jp-NotebookTools .jp-Collapse {
-  margin-top: 16px;
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-
-/*-----------------------------------------------------------------------------
-| Presentation Mode (.jp-mod-presentationMode)
-|----------------------------------------------------------------------------*/
-
-.jp-mod-presentationMode .jp-Notebook {
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-}
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-.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
-.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
-  flex: 0 0 110px;
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-
-/*-----------------------------------------------------------------------------
-| Side-by-side Mode (.jp-mod-sideBySide)
-|----------------------------------------------------------------------------*/
-.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
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-  margin-left: 5%;
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-}
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-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
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-  grid-template-columns: minmax(0, 1fr) min-content minmax(
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-  grid-template-rows: auto minmax(0, 1fr) auto;
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-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
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-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
-  grid-area: header;
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-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
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-
-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
-  /* overwrite the default margin (no vertical separation needed in side by side move */
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-}
-
-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
-  grid-area: footer;
-}
-
-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
-  grid-area: handle;
-  user-select: none;
-  display: block;
-  height: 100%;
-  cursor: ew-resize;
-  padding: 0 var(--jp-cell-padding);
-}
-
-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
-  content: '';
-  display: block;
-  background: var(--jp-border-color2);
-  height: 100%;
-  width: 5px;
-}
-
-.jp-mod-sideBySide.jp-Notebook
-  .jp-CodeCell.jp-mod-resizedCell
-  .jp-CellResizeHandle::after {
-  background: var(--jp-border-color0);
-}
-
-.jp-CellResizeHandle {
-  display: none;
-}
-
-/*-----------------------------------------------------------------------------
-| Placeholder
-|----------------------------------------------------------------------------*/
-
-.jp-Cell-Placeholder {
-  padding-left: 55px;
-}
-
-.jp-Cell-Placeholder-wrapper {
-  background: #fff;
-  border: 1px solid;
-  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
-  border-radius: 4px;
-  -webkit-border-radius: 4px;
-  margin: 10px 15px;
-}
-
-.jp-Cell-Placeholder-wrapper-inner {
-  padding: 15px;
-  position: relative;
-}
-
-.jp-Cell-Placeholder-wrapper-body {
-  background-repeat: repeat;
-  background-size: 50% auto;
-}
-
-.jp-Cell-Placeholder-wrapper-body div {
-  background: #f6f7f8;
-  background-image: -webkit-linear-gradient(
-    left,
-    #f6f7f8 0%,
-    #edeef1 20%,
-    #f6f7f8 40%,
-    #f6f7f8 100%
-  );
-  background-repeat: no-repeat;
-  background-size: 800px 104px;
-  height: 104px;
-  position: absolute;
-  right: 15px;
-  left: 15px;
-  top: 15px;
-}
-
-div.jp-Cell-Placeholder-h1 {
-  top: 20px;
-  height: 20px;
-  left: 15px;
-  width: 150px;
-}
-
-div.jp-Cell-Placeholder-h2 {
-  left: 15px;
-  top: 50px;
-  height: 10px;
-  width: 100px;
-}
-
-div.jp-Cell-Placeholder-content-1,
-div.jp-Cell-Placeholder-content-2,
-div.jp-Cell-Placeholder-content-3 {
-  left: 15px;
-  right: 15px;
-  height: 10px;
-}
-
-div.jp-Cell-Placeholder-content-1 {
-  top: 100px;
-}
-
-div.jp-Cell-Placeholder-content-2 {
-  top: 120px;
-}
-
-div.jp-Cell-Placeholder-content-3 {
-  top: 140px;
-}
-
-</style>
-<style type="text/css">
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*
-The following CSS variables define the main, public API for styling JupyterLab.
-These variables should be used by all plugins wherever possible. In other
-words, plugins should not define custom colors, sizes, etc unless absolutely
-necessary. This enables users to change the visual theme of JupyterLab
-by changing these variables.
-
-Many variables appear in an ordered sequence (0,1,2,3). These sequences
-are designed to work well together, so for example, `--jp-border-color1` should
-be used with `--jp-layout-color1`. The numbers have the following meanings:
-
-* 0: super-primary, reserved for special emphasis
-* 1: primary, most important under normal situations
-* 2: secondary, next most important under normal situations
-* 3: tertiary, next most important under normal situations
-
-Throughout JupyterLab, we are mostly following principles from Google's
-Material Design when selecting colors. We are not, however, following
-all of MD as it is not optimized for dense, information rich UIs.
-*/
-
-:root {
-  /* Elevation
-   *
-   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
-   *
-   * https://github.com/material-components/material-components-web
-   * https://material-components-web.appspot.com/elevation.html
-   */
-
-  --jp-shadow-base-lightness: 0;
-  --jp-shadow-umbra-color: rgba(
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    0.2
-  );
-  --jp-shadow-penumbra-color: rgba(
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    0.14
-  );
-  --jp-shadow-ambient-color: rgba(
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    0.12
-  );
-  --jp-elevation-z0: none;
-  --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
-    0 1px 1px 0 var(--jp-shadow-penumbra-color),
-    0 1px 3px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
-    0 2px 2px 0 var(--jp-shadow-penumbra-color),
-    0 1px 5px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
-    0 4px 5px 0 var(--jp-shadow-penumbra-color),
-    0 1px 10px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
-    0 6px 10px 0 var(--jp-shadow-penumbra-color),
-    0 1px 18px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
-    0 8px 10px 1px var(--jp-shadow-penumbra-color),
-    0 3px 14px 2px var(--jp-shadow-ambient-color);
-  --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
-    0 12px 17px 2px var(--jp-shadow-penumbra-color),
-    0 5px 22px 4px var(--jp-shadow-ambient-color);
-  --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
-    0 16px 24px 2px var(--jp-shadow-penumbra-color),
-    0 6px 30px 5px var(--jp-shadow-ambient-color);
-  --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
-    0 20px 31px 3px var(--jp-shadow-penumbra-color),
-    0 8px 38px 7px var(--jp-shadow-ambient-color);
-  --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
-    0 24px 38px 3px var(--jp-shadow-penumbra-color),
-    0 9px 46px 8px var(--jp-shadow-ambient-color);
-
-  /* Borders
-   *
-   * The following variables, specify the visual styling of borders in JupyterLab.
-   */
-
-  --jp-border-width: 1px;
-  --jp-border-color0: var(--md-grey-400);
-  --jp-border-color1: var(--md-grey-400);
-  --jp-border-color2: var(--md-grey-300);
-  --jp-border-color3: var(--md-grey-200);
-  --jp-inverse-border-color: var(--md-grey-600);
-  --jp-border-radius: 2px;
-
-  /* UI Fonts
-   *
-   * The UI font CSS variables are used for the typography all of the JupyterLab
-   * user interface elements that are not directly user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-  --jp-ui-font-scale-factor: 1.2;
-  --jp-ui-font-size0: 0.83333em;
-  --jp-ui-font-size1: 13px; /* Base font size */
-  --jp-ui-font-size2: 1.2em;
-  --jp-ui-font-size3: 1.44em;
-  --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
-    helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
-    'Segoe UI Symbol';
-
-  /*
-   * Use these font colors against the corresponding main layout colors.
-   * In a light theme, these go from dark to light.
-   */
-
-  /* Defaults use Material Design specification */
-  --jp-ui-font-color0: rgba(0, 0, 0, 1);
-  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
-  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
-  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
-
-  /*
-   * Use these against the brand/accent/warn/error colors.
-   * These will typically go from light to darker, in both a dark and light theme.
-   */
-
-  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
-  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
-  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
-  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
-
-  /* Content Fonts
-   *
-   * Content font variables are used for typography of user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-  --jp-content-line-height: 1.6;
-  --jp-content-font-scale-factor: 1.2;
-  --jp-content-font-size0: 0.83333em;
-  --jp-content-font-size1: 14px; /* Base font size */
-  --jp-content-font-size2: 1.2em;
-  --jp-content-font-size3: 1.44em;
-  --jp-content-font-size4: 1.728em;
-  --jp-content-font-size5: 2.0736em;
-
-  /* This gives a magnification of about 125% in presentation mode over normal. */
-  --jp-content-presentation-font-size1: 17px;
-  --jp-content-heading-line-height: 1;
-  --jp-content-heading-margin-top: 1.2em;
-  --jp-content-heading-margin-bottom: 0.8em;
-  --jp-content-heading-font-weight: 500;
-
-  /* Defaults use Material Design specification */
-  --jp-content-font-color0: rgba(0, 0, 0, 1);
-  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
-  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
-  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
-  --jp-content-link-color: var(--md-blue-900);
-  --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
-    'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
-    'Segoe UI Emoji', 'Segoe UI Symbol';
-
-  /*
-   * Code Fonts
-   *
-   * Code font variables are used for typography of code and other monospaces content.
-   */
-
-  --jp-code-font-size: 13px;
-  --jp-code-line-height: 1.3077; /* 17px for 13px base */
-  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
-  --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
-  --jp-code-font-family: var(--jp-code-font-family-default);
-
-  /* This gives a magnification of about 125% in presentation mode over normal. */
-  --jp-code-presentation-font-size: 16px;
-
-  /* may need to tweak cursor width if you change font size */
-  --jp-code-cursor-width0: 1.4px;
-  --jp-code-cursor-width1: 2px;
-  --jp-code-cursor-width2: 4px;
-
-  /* Layout
-   *
-   * The following are the main layout colors use in JupyterLab. In a light
-   * theme these would go from light to dark.
-   */
-
-  --jp-layout-color0: white;
-  --jp-layout-color1: white;
-  --jp-layout-color2: var(--md-grey-200);
-  --jp-layout-color3: var(--md-grey-400);
-  --jp-layout-color4: var(--md-grey-600);
-
-  /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-  --jp-inverse-layout-color0: #111;
-  --jp-inverse-layout-color1: var(--md-grey-900);
-  --jp-inverse-layout-color2: var(--md-grey-800);
-  --jp-inverse-layout-color3: var(--md-grey-700);
-  --jp-inverse-layout-color4: var(--md-grey-600);
-
-  /* Brand/accent */
-
-  --jp-brand-color0: var(--md-blue-900);
-  --jp-brand-color1: var(--md-blue-700);
-  --jp-brand-color2: var(--md-blue-300);
-  --jp-brand-color3: var(--md-blue-100);
-  --jp-brand-color4: var(--md-blue-50);
-  --jp-accent-color0: var(--md-green-900);
-  --jp-accent-color1: var(--md-green-700);
-  --jp-accent-color2: var(--md-green-300);
-  --jp-accent-color3: var(--md-green-100);
-
-  /* State colors (warn, error, success, info) */
-
-  --jp-warn-color0: var(--md-orange-900);
-  --jp-warn-color1: var(--md-orange-700);
-  --jp-warn-color2: var(--md-orange-300);
-  --jp-warn-color3: var(--md-orange-100);
-  --jp-error-color0: var(--md-red-900);
-  --jp-error-color1: var(--md-red-700);
-  --jp-error-color2: var(--md-red-300);
-  --jp-error-color3: var(--md-red-100);
-  --jp-success-color0: var(--md-green-900);
-  --jp-success-color1: var(--md-green-700);
-  --jp-success-color2: var(--md-green-300);
-  --jp-success-color3: var(--md-green-100);
-  --jp-info-color0: var(--md-cyan-900);
-  --jp-info-color1: var(--md-cyan-700);
-  --jp-info-color2: var(--md-cyan-300);
-  --jp-info-color3: var(--md-cyan-100);
-
-  /* Cell specific styles */
-
-  --jp-cell-padding: 5px;
-  --jp-cell-collapser-width: 8px;
-  --jp-cell-collapser-min-height: 20px;
-  --jp-cell-collapser-not-active-hover-opacity: 0.6;
-  --jp-cell-editor-background: var(--md-grey-100);
-  --jp-cell-editor-border-color: var(--md-grey-300);
-  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-  --jp-cell-editor-active-background: var(--jp-layout-color0);
-  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-  --jp-cell-prompt-width: 64px;
-  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-  --jp-cell-prompt-letter-spacing: 0;
-  --jp-cell-prompt-opacity: 1;
-  --jp-cell-prompt-not-active-opacity: 0.5;
-  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
-
-  /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-  --jp-cell-inprompt-font-color: #307fc1;
-
-  /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-  --jp-cell-outprompt-font-color: #bf5b3d;
-
-  /* Notebook specific styles */
-
-  --jp-notebook-padding: 10px;
-  --jp-notebook-select-background: var(--jp-layout-color1);
-  --jp-notebook-multiselected-color: var(--md-blue-50);
-
-  /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-  --jp-notebook-scroll-padding: calc(
-    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-  );
-
-  /* Rendermime styles */
-
-  --jp-rendermime-error-background: #fdd;
-  --jp-rendermime-table-row-background: var(--md-grey-100);
-  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
-
-  /* Dialog specific styles */
-
-  --jp-dialog-background: rgba(0, 0, 0, 0.25);
-
-  /* Console specific styles */
-
-  --jp-console-padding: 10px;
-
-  /* Toolbar specific styles */
-
-  --jp-toolbar-border-color: var(--jp-border-color1);
-  --jp-toolbar-micro-height: 8px;
-  --jp-toolbar-background: var(--jp-layout-color1);
-  --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
-  --jp-toolbar-header-margin: 4px 4px 0 4px;
-  --jp-toolbar-active-background: var(--md-grey-300);
-
-  /* Statusbar specific styles */
-
-  --jp-statusbar-height: 24px;
-
-  /* Input field styles */
-
-  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-  --jp-input-active-background: var(--jp-layout-color1);
-  --jp-input-hover-background: var(--jp-layout-color1);
-  --jp-input-background: var(--md-grey-100);
-  --jp-input-border-color: var(--jp-inverse-border-color);
-  --jp-input-active-border-color: var(--jp-brand-color1);
-  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-  /* General editor styles */
-
-  --jp-editor-selected-background: #d9d9d9;
-  --jp-editor-selected-focused-background: #d7d4f0;
-  --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-  /* Code mirror specific styles */
-
-  --jp-mirror-editor-keyword-color: #008000;
-  --jp-mirror-editor-atom-color: #88f;
-  --jp-mirror-editor-number-color: #080;
-  --jp-mirror-editor-def-color: #00f;
-  --jp-mirror-editor-variable-color: var(--md-grey-900);
-  --jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
-  --jp-mirror-editor-variable-3-color: #085;
-  --jp-mirror-editor-punctuation-color: #05a;
-  --jp-mirror-editor-property-color: #05a;
-  --jp-mirror-editor-operator-color: #a2f;
-  --jp-mirror-editor-comment-color: #408080;
-  --jp-mirror-editor-string-color: #ba2121;
-  --jp-mirror-editor-string-2-color: #708;
-  --jp-mirror-editor-meta-color: #a2f;
-  --jp-mirror-editor-qualifier-color: #555;
-  --jp-mirror-editor-builtin-color: #008000;
-  --jp-mirror-editor-bracket-color: #997;
-  --jp-mirror-editor-tag-color: #170;
-  --jp-mirror-editor-attribute-color: #00c;
-  --jp-mirror-editor-header-color: blue;
-  --jp-mirror-editor-quote-color: #090;
-  --jp-mirror-editor-link-color: #00c;
-  --jp-mirror-editor-error-color: #f00;
-  --jp-mirror-editor-hr-color: #999;
-
-  /*
-    RTC user specific colors.
-    These colors are used for the cursor, username in the editor,
-    and the icon of the user.
-  */
-
-  --jp-collaborator-color1: #ffad8e;
-  --jp-collaborator-color2: #dac83d;
-  --jp-collaborator-color3: #72dd76;
-  --jp-collaborator-color4: #00e4d0;
-  --jp-collaborator-color5: #45d4ff;
-  --jp-collaborator-color6: #e2b1ff;
-  --jp-collaborator-color7: #ff9de6;
-
-  /* Vega extension styles */
-
-  --jp-vega-background: white;
-
-  /* Sidebar-related styles */
-
-  --jp-sidebar-min-width: 250px;
-
-  /* Search-related styles */
-
-  --jp-search-toggle-off-opacity: 0.5;
-  --jp-search-toggle-hover-opacity: 0.8;
-  --jp-search-toggle-on-opacity: 1;
-  --jp-search-selected-match-background-color: rgb(245, 200, 0);
-  --jp-search-selected-match-color: black;
-  --jp-search-unselected-match-background-color: var(
-    --jp-inverse-layout-color0
-  );
-  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-  /* Icon colors that work well with light or dark backgrounds */
-  --jp-icon-contrast-color0: var(--md-purple-600);
-  --jp-icon-contrast-color1: var(--md-green-600);
-  --jp-icon-contrast-color2: var(--md-pink-600);
-  --jp-icon-contrast-color3: var(--md-blue-600);
-
-  /* Button colors */
-  --jp-accept-color-normal: var(--md-blue-700);
-  --jp-accept-color-hover: var(--md-blue-800);
-  --jp-accept-color-active: var(--md-blue-900);
-  --jp-warn-color-normal: var(--md-red-700);
-  --jp-warn-color-hover: var(--md-red-800);
-  --jp-warn-color-active: var(--md-red-900);
-  --jp-reject-color-normal: var(--md-grey-600);
-  --jp-reject-color-hover: var(--md-grey-700);
-  --jp-reject-color-active: var(--md-grey-800);
-
-  /* File or activity icons and switch semantic variables */
-  --jp-jupyter-icon-color: #f37626;
-  --jp-notebook-icon-color: #f37626;
-  --jp-json-icon-color: var(--md-orange-700);
-  --jp-console-icon-background-color: var(--md-blue-700);
-  --jp-console-icon-color: white;
-  --jp-terminal-icon-background-color: var(--md-grey-800);
-  --jp-terminal-icon-color: var(--md-grey-200);
-  --jp-text-editor-icon-color: var(--md-grey-700);
-  --jp-inspector-icon-color: var(--md-grey-700);
-  --jp-switch-color: var(--md-grey-400);
-  --jp-switch-true-position-color: var(--md-orange-900);
-}
-</style>
-<style type="text/css">
-/* Force rendering true colors when outputing to pdf */
-* {
-  -webkit-print-color-adjust: exact;
-}
-
-/* Misc */
-a.anchor-link {
-  display: none;
-}
-
-/* Input area styling */
-.jp-InputArea {
-  overflow: hidden;
-}
-
-.jp-InputArea-editor {
-  overflow: hidden;
-}
-
-.cm-editor.cm-s-jupyter .highlight pre {
-/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
-  padding: var(--jp-code-padding) 4px;
-  margin: 0;
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-<main><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">lambeq.backend</span> <span class="kn">import</span> <span class="n">Ty</span><span class="p">,</span> <span class="n">Box</span><span class="p">,</span> <span class="n">Word</span><span class="p">,</span><span class="n">Frame</span><span class="p">,</span> <span class="n">Swap</span>
-<span class="kn">from</span> <span class="nn">lambeq_discocirc</span> <span class="kn">import</span> <span class="n">DisCoCircReader</span>
-<span class="n">s</span> <span class="o">=</span> <span class="n">Ty</span><span class="p">(</span><span class="s1">'s'</span><span class="p">)</span>
-<span class="n">n</span> <span class="o">=</span> <span class="n">Ty</span><span class="p">(</span><span class="s1">'n'</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">file_path</span> <span class="o">=</span> <span class="s1">'train-00.txt'</span>
-<span class="n">reader</span> <span class="o">=</span> <span class="n">DisCoCircReader</span><span class="p">()</span>
-<span class="c1"># Open the file using a context manager</span>
-<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
-    <span class="c1"># Iterate through each line in the file</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' sample -&gt; </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">, </span><span class="se">\n</span><span class="s1"> </span><span class="si">{</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
-            <span class="n">diagram</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">text2circuit</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
-            <span class="n">diagram</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">foliated</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">draw_type_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">color_boxes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">color_wire</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-            <span class="c1"># Skip lines that cause exceptions and print an error message</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Skipping line </span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> due to an error: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
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-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-<pre> sample -&gt; 0, 
- Me no sabby no way insisted Chow . Oh yes you do snapped Dave . But you wo n't tell .
-huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-To disable this warning, you can either:
-	- Avoid using `tokenizers` before the fork if possible
-	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-</pre>
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"/>
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-<pre> sample -&gt; 1, 
- No replied Darrin with great promptness . You 're going to do that yourself . What gasped Midshipman Pennington in intense astonishment .
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-<pre> sample -&gt; 2, 
- In what way shall I do it Hallam . Ca n't you suggest something . Yes retorted Hallam with great energy .
-Skipping line 3 due to an error: Bobcat failed to parse "Ca n't you suggest something .".
- sample -&gt; 3, 
- What was that asked Dr Mackenzie glancing at him sharply . Did you try the aid of liquor . Worse I 'm afraid sir .
-Skipping line 4 due to an error: Bobcat failed to parse "Worse I ' m afraid sir .".
- sample -&gt; 4, 
- Besides it 's a serious breach of discipline . I shall have to report you Mr Pennington . I expected it sir replied Pennington meekly .
-</pre>
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-<pre> sample -&gt; 5, 
- Unfortunately you are quite right smiled Dave . Still I 'd do the same thing over again . Oh of course you would assented Dan .
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-<pre> sample -&gt; 6, 
- That 's because you 're Dave Darrin . Here a voice like a bass horn was heard . All third classmen report to the quarter deck immediately .
-</pre>
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-<pre> sample -&gt; 7, 
- Then his attention began to wander . A brilliantly shining brass rod near him caught his eye . Something about the glossy metal fascinated him .
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x+vH374Qeedd54+//xzde7cWe+++64kaeTIkVq/fr3+9Kc/aeXKlerVq5eeffbZ0LojR47UrFmzVFxcLEk6++yz5XQ6w7IfQENAIAYAAAAAwBFq27at7Ha7Vq1aFZq2e/durV69er/Lu91ubd68WZs3bw5NW7VqlXbt2qXOnTuHpnXo0EF33HGHPv74Yw0dOlTTp08PzUtLS9MNN9ygd955R2PGjNG0adNC884991zFxsbqrbfekiRdcMEFtbavQGNEH2IAAAAAAByh+Ph4DR8+XFOmTJEkffLJJ7rrrrskSTt27FB2drZKS0u1ZcsWZWdnKzk5We3atdMFF1ygMWPGyO/36+9//7t69uwpi8WiN954Q9OnT9cll1yiVq1aafv27fr666915plnKjs7W0899ZT69eunNm3aaM+ePZozZ45atGih7OzsUE2DBg0KXTXWtWvXY39QgAaEQAwAAAAAgKMwadIk7dixQz///LPGjh0bmv7666/r9ddflyRNnDhREydOrLZeVlZWtfGePXuGhj/55JNq815++WW9/PLLkqQ33nij2rzvv/9+n+UlyW63y+VyHcUeAZGDQAwAAAAAgKMQHx+v2bNny+v1yufzae/evRo0aJDuuOMOXXjhhUe0rQcffFDr16/XjBkzjrqeZcuW6YYbbtDChQv/v707j5Hzvu87/nlmZmcPHktyh6QOe2VboiTLl45EUhMHdlsngIEURQ44tasWTtv80QRwASf5K00CNIWN2m7sGug/boAmSOA0aYMaSdo0beLUsR0fNSU5trWSHFmRLFkSd0lxSZF7zczTP2b24mEdlnZI/l4v4MHzzDPPPM93l2vC+9Yzw8zOzr7k80AJBDEAAAB4Ce677748+OCDufPOO5MkH/7wh9NsNvO+973vRd+hNTMzk2eeeSa33377i55jZWUl8/Pz+eQnP5l3vetd2+44Ay7Mh+oDAADAS/SRj3wkb3nLW/KOd7wjZ86cyWc/+9kdf7vi7/7u7+a6667LyZMn86EPfWhHrw2XK3eIAQAAwEtw22235ejRo6MeI+9973vz3ve+d9RjwGXFHWIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUfwrkwAAABRtbm5u1CPk+PHjOXPmTO69995Rj3JJfD/glSaIAQAAUKROp5Opqancc889ox5lwx133DHqEZIkU1NT6XQ6ox4DXjGCGAAAAEWanZ3N3NxcFhYWRj3KJafT6WR2dnbUY8ArRhADAACgWLOzs8IPFMiH6gMAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABSlNeoBAAAA4Fxzc3OjHoEd4M+ZURHEAAAAuGR0Op1MTU3lnnvuGfUo7JCpqal0Op1Rj0FhBDEAAAAuGbOzs5mbm8vCwsL3dJ73v//96ff7+djHPvbyDMY2H//4x/PpT386n/rUp77nc3U6nczOzn7vQ8GLIIgBAABwSZmdnf2eA8m+ffvS6/Vy++23v0xTsdXhw4czPj7u+8tly4fqAwAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACK0hr1AAAAAFxZ5ubmRj1Crr/++tR1nXvvvXfUo1yR9u3bl7vvvvuS+P5eCj9vXH4EMQAAAF4WnU4nU1NTueeee0Y9yoaPfvSjox7hivY7v/M7ox4hSTI1NZVOpzPqMbiMCGIAAAC8LGZnZzM3N5eFhYVRj0JhOp1OZmdnRz0GlxFBDAAAgJfN7OysMAFc8nyoPgAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFFaox4AAACAl25ubm5HrrP8xONpjI2lffjqHbleaRpPnki12k3vtYdGPcoVaeH0Wuaf6+b1V0/uyPV26n+XvHSCGAAAwGWo0+lkamoq99xzz45c719fuz9Pr/XyG8dO7cj1SvOh1ptzpNqTH1v7/KhHuTIdeWeqm3409R//3I5dcmpqKp1OZ8eux4sjiAEAAFyGZmdnMzc3l4WFhR253qO/8gtpX3VN/uXPvn9Hrlea3R/4VFqPzufof/r4qEe5Iv32F+bzm3+1kD8/enTHrtnpdDI7O7tj1+PFEcQAAAAuU7Ozszv2C/ex3buzd2Ymt99++45crzQnZj6XtfmzeZ3v7yvi049/Pc3Ws35+2eBD9QEAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAF6Qfrc76hGuWHW3N+oRoCiCGAAAAM/r0B135tv/508y958/kTNPPTnqca4Y3ScWcvo//mGW/vvn0/47t4x6HChGa9QDAAAAcOl7w7/42awuLuYrH/zVfOUDv5Kpw1enc+vt6dx6R/bfdEsmOgczMdPJxP4DaYyNjXrcS0rd7aW/cCq9+ZPpP3Myq1//26x+6cGsfvmh9J48niTZ9dM/kr2/8BMjnhTKIYgBAADwvMb3H8jd//bDecu/+sXM3380C8Plqx//cHpLS9uObe/bn4kDnUzMzAwi2fpyoJP29L40x8fTbLfTaI9v2W6nOT6xud3e3K6qake/1rquk9Vu6pW1wbK8mnp1LVlZS728trl/ZTX1ylqy0k3/5HPpHTuZ/vziIHwdW0xvfjH9+ZPpHz+97fzVZDvtO45k6t1vT/vOmzJ+581pXrV/R79GKJ0gBgAAwAs2efBQZn/4nZn94XcmGXyu2JmnnszKieNZPr4wWLZuH5/P6W89kqWF+aycPJG633/R12xUzeHSSCONJFWqDCLZ+nrbdp1USd5w/MjG9rYjNvZVG9tVf3NpvJSP82pWqQ7sSfPQvjSvOpDmtZ2M3XZ9mgen0zi4L81Dw3Vnb5qvPpiq1XwJFwFeLoIYAAAAL0m9spa1r34rjYeeSHt+Ma35k5k8Nrgrqje/mP6xs+nNryWru5PsTp3XpFf106/6qas6/fTTH6tSt5up243U7Ub6Y1X6rSr1WCN1q0q/mdStpN9M+o2krurUVbJeturNBpb1ulUPH/U6w7uuqsFTdbX1yM11neEsdS+99NKvu+n1u+n2u+n3V9PtrqXbX0uvt5pudyXd7mp6/bXBq6o6/aqfXtUfXOdU0lhqZ+JUJxMnO5k4PpOJ+U4mnt68U27vyRty4JY3pjk+vjN/UMB5BDEAAABekHqtm6U/+lJWvvBAVr/8UFbveyRZHfzLk9X0ruHdUNNpHtqX9h1HBo8P7dt+l9S+3akm2sl4K9X4WKrm5XmnVL/XS391Jb3V1fRWVrJ2anF4d9zClrvjFrJ8/Hie+/bjWbj/3iwfX8jqqcUkSWOsnQNveFM6t96RQ7d/X179jnf67DXYQYIYAAAAz2v16Ddz/Gc+lu4Dj6f5msMZv+vmTP3U29K+86a03/iaQeQqSKPZTGNyKq3JqcGOQ4czfcONz/u63spyTj78YObvP5r5+47miT//0zz4m5/I9A035gf+3X9I5823vcKTA4kgBgAAwAtw9g8+l2p8LIe/8NG0b7th1ONctprjE5l5062ZedOtufmf/PMkyYkHvp4v/NLP57H/+UeCGOyQxqgHAAAA4NK38oW5jL3xNWLYK+DALW/M/ptvybGjXxr1KFAMQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxACAy0PVSOpu6hMPpu4ujXoagGLUK2tZm3s8/WefS9VqjnqcK1aj2Urd7416jCvW6aVu2k0JhE2tUQ8AAPCC3Pye5OHfT/7kPUmSeupwsve6ZM91yZ5XJe29ydiuLcvuze3WVKrK/wkGSJK6rlOfWU596mz6p8+mf2ppY7s+dTb9U2fTffTpdL/5ZNYefjK9x+eTfj/Vrons/eN/M+rxr1idt9yWb/7eb+dzP/9zuf7Hfyozb7417T17Rz3WZW2t289fP/Zs/vS+J/Prf/RA3v7Gw6MeiUuIIAYAXBaq1kTqf/AHyeIjyanHNpf5+5JH/zh5nrvG6tauiwezrY9bE0ljLGm0tq+rVtJsDdfDxxc6btu6JcQBF1XXdbLWTb3WS1a7qde6qVe7w33r+9e2P7/WTdZ6qVfXhuv1fd3US6vpbw1bp8+mPrU0jF7r+5ZSn15K+v2LzlVNttOcPZTWkWsz9eM/mNYN16R15NqMveG6NA/s2cHvUFmu/4l/lCS5799/II/+4R8kVZXpG27MwVvvyIE3vCmTBw9nYqazsYzt3pOqqkY89egtr/ZybHEpxxaXN5a5JxbzpYfn85VHjmdptZexViM/844j+bX33DbqcbmEVHVd16MeAnh+//fbj+Tv/f4n8vA/+8XcsL8z6nEALjl1v5usnU3WnkvWzmxZnku6W7bPfW7b4zODsFa/jG9ZqZrbAtlFA9q20NY65zXPE95SJVU1eFtphuuN7SpJY/P5q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-<pre> sample -&gt; 8, 
- Then point him out to me insisted the officer . There was hardly any need to do so . Pennington 's face flushed with mortification was sufficient identification .
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-<pre> sample -&gt; 9, 
- I shall call you out Mr Darrin . You 'll find my hearing excellent smiled Dave . I shall make but one stipulation .
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-<pre> sample -&gt; 10, 
- What did you say particularly . That you would have to fight me . I have already signified my entire willingness Mr Pennington .
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"/>
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-<pre> sample -&gt; 11, 
- With two very stiff nods the midshipmen parted . Pennington hastened at once in search of Hallam . Will you serve me old man queried Pennington .
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-<pre> sample -&gt; 12, 
- You 'll be hammered to a pulp Pen . No one has yet beaten Darrin at a fist fight . There always has to be a first time you know .
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-<pre> sample -&gt; 13, 
- And you think you 're It . As far as Darrin is concerned yes . Too bad too bad sighed Hallam .
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-<pre> sample -&gt; 14, 
- Then we can come back after the others . That 's agreeable to me nodded Midshipman Decker . In twenty minutes the two seconds were back .
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-<pre> sample -&gt; 15, 
- Dave still used his footwork and most cleverly . Yet wherever he went Pen followed him nimbly . It did n't look so one sided now .
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N3+frkk6cCvWdqavlLb0ldq81fL9P2Lb0uSTCaT6jRLUP02Z6l+6+aqn9hc9sSS5zqOBJnN3PcEAAAAACoCgRgAVLA6TRupa8pl6ppymSQpb88+7dm4veSxKVN7Nmdqy5If9MMr8+UrLJJU0j+ZvXWzkoCsdXPVb3OW4lo0VXSDOMU0sCuydgyd9wMAAADAaSIQA4BKFl2/nqLr19NZ53UtNd7v82n/L7u0Z1OmPJuOhGWbMrXmnU90wL2rVNuwiHBFx8cdCcjiFB1vL3lucOQ5OM2uqHq1OdsMAAAAAI5BIAYAVYTZYlFci6aKa9FUGtin1LRC72EdcGcpd7dHebv3Ki97r3J371Xebo9ys/dq989btOXrH5S3e6+KDueXXm5YmKLr11V0A7uij4RlUbHRioiNVmRstCJiagVfR8bW+sNwtMIiIzgbDQAAAECNQiAGANVAuC1K8W1bKL5tiz9tFwgEVJB3SHm7jwnMjjznZe9TbrZH+7fvUFZOnvJz8lSQe0j5B/PkLy4+6TLNFsuRgKwkKIuIqRUMyyKiaykitmQ4IjZa4bWiFBYRrrCIcFnCrQoLt8oSEV7q+eg0yzGvg+OsVsI3AAAAABWOQAwAahCTyaTImGhFxkTL3qrZKc0TCARUXFCogpw85eceKnnOyVPB0cAs51Dp4dyS6d59B7X/l13Bafk5eSrM8yoQCJzRNpQKyk4QqAWfTxCohYWfPGwL+8N85jCLZDLJZDaXPEwmmcymI8/mI9N+Hz46XScYNp+g/dF25pPMc+rrNEsmHTdsPmYdxy3jyAMAAADAiRGIAUCIM5lMskZGyBoZoej4uDNenq+4WL7CIhUXFJZ6/uO44sIi+Y4+n6D9yebzFRapuPDoc5EK8rzy7jt4avMVFJbDO1Z9/FmoZjoyfGyodtKg8WRn9Vn/PLz8s7MBT2U+c1gYwR4AnAKXy2V0CTBIkyZN1LdvX2VkZBhdCgzEZ8DpIRADAJQrS1iYLGFhCrdFGV3KcQKBgPxHAjtfUbECgYACfr8UkAJ+f3C45Dkg/WH49/bHD/uPa//7cv0nmSfgD/w+7WTrLLW8v1iv36/AkXWWTDvBth1pf1xNPl8wnDxR+Hh0XEGet0xB5pkwmUwnDNTCIiOOXKZb6/dLeGOi/3BJb+lLfI/tMy8sIpygDUCNYLfbZbPZlJKSYnQpMNhzzz1ndAkwmM1mk91uN7qMaoVADAAQMkwmkyzWkr7KUPGOBpC/h2vHnBVYhlDtj+2LvPnBy3cLcg8p9zfPkct2D6ngyGW/fp/vpHVZrNbf+74rFZjVUlSd2sE7tAbv4nrk7q0R0bUq8d0DgL/mcDjkcrnk8XiMLgWAwex2uxwOh9FlVCsEYgAAoEIYFUAGAgEVHT4Smh3Tx11BrvcPw0f6zMstCdPysvcp+3/bgzeh+GOoZrVFHQnISu7WGhP/e1j2+3NJmBZZO4az0ABUCofDwZdgADgNBGIAAKBGMZlMCrdFKdwWpZgGp3fpgN/v1+F9B5W723Pkrq1/eM7eq51rfj5yF9e98hUVlZrfaouSvXUz1U9srvqtm8ue2Fzxbc6SPbG5bHVrl8dmAgAA4AwQiAEAAPyB2WxWLXtd1bLXldq3/tO2gUBAhw/k/B6YZe/TgV+z5Nn8i/Zs2q5fvlujgzt3B9vXstctCcgSSwKy+kceca2ayRoZUdGbBgAAABGIAQAAnBGTySRb3dqy1a2t+LYtTtimIO+QPFvc8mzKVPbG7fJsztRvP2/Rhg++VP7B3OBy4p0t1eL87mqZfI5a9O1+2me4AQAA4M8RiAEAAFSwiOhaatzZqcadnaXGBwIBHdqzT3s2ZWrPxu1yL1+nLUu+1/cvvSNJqt/mLLXsWxKOtfpbTwIyAACAckIgBgAAYBCTyaTo+JJO+s86r6vOGXWFJOngrt3avnSVtqat0LalK/XDK/NlMpvVdlAfnTPqSjkv6cvdUgEAAM4AgRgAAEAVUzuhgTpfc4k6X3OJJCnntz366cPFWvH6fzR32BhFN7BrwOQx6jH6KpnNZoOrBQAAqH44ggIAAKjiYhvW17m3XqN/rviP7lzzodoMPE/v3zZFL5x/nbI3bjO6PAAAgGqHQAwAAKAaSejUVtfMfly3fv2GDmXv02uX3KLDRzrmBwAAwKkhEAMAAKiGWvY9Rzd//pq8ew/ov3c9bnQ5AAAA1Qp9iAGnIaGzU4cP5srv88lssRhdTqXbuG+PejRyqHZEhNGlAEBIq3dWEw165E6tnL1AX059Xo4eHdW0ewfZ6tUxujSgWvPneFWYsUWFqzYpcChf1s4tK23dLper0taF6u+nXV7tO1SsPq1jjS4F1QifMyUIxIDT0KRLO/133OP65bs1at2/l1r9rYdanN9dterXk8lkMrq8clfoK9aa7F1a4t6qr91blL5ju5xx8WpTL97o0gAg5HW+5mJt/OJbfTPjDR3ef1CSFB0fp1r2uqplryNbXF3ViqujWva6sh15Pva1rV5tWaMiZYkIp4N+1DiBQEAqKFLgcKH8+/Pk25cj/94c+T058u/Lld+TI9/e31/79x4Z3n1ACgRkio5SePdERV1yToXXarfbZbPZlJKSUuHrQs1h6nidVN+pwOKJRpeCasZms8lutxtdhqFMgUAgYHQRQHW0fVmGMlIXasuS7+XZ/IskyRoVqdiE+N8fjeortlHJ65iGdoXXipI1KlLWqAiFRUaUem0Jq9h8OhAIqMBXrMPFRTpcXKz84qLg68PFRdqX79WuvBztOpSjXXk5ysrL0a5DudqVlyPP4UOSpGhruPo2baH+zVrr1o49FVHBNQMATl0gENDerW65l6/Xvu07dMizX4f27pd374GS156S1wW5h066DEu4VWER4SV/oyIjFBYZ/ofhCIVFWEueIyMUFn50WviRab8PWyLCf5/n6LQIq8xhYTKZzTJbzL8/WywnHGc2m2U6dpzZLLPF8ufjCPXKTcDvl/wByecvee07ZtjnO2ZaQPL5gtNL5vMfM81/kmX4FSgsKgms8gsVyD/yXFj0++v8QunY4YKikseR9iooUqDgyLwFx7Q58qyCopNun8kWIXNcrMz2WJnrxcgSfB0rS9P6iujeWmFtmshUiVcDuN1ueTyeSlsfqr9pX2Rp9S95eufm1kaXgmrGbrfL4XAYXYahCMSAcnDg1yz98sM65ezcrZysPcrJylbOrmwd2LlbObuyVfgnXz6OCljMCoRbFQi3yG8Nky/cooDZrIBJkkwKmKXAkbPPAiaT1l7eRVmRpt+nm0pelzxMKjabVBxmUpHZpEKzSUWn8P0gzGRSQ1uMGsfUVqPoWDWqFauE6FglRMeobb14dWvQRNYQvEQUAGqS4oLCUkGZd99BFecXqji/QEX5BSouKHldnF+oovwC+QoKj4wvGVd8dDj/9+HgvMcMF+cXyKjDzGBIZjaVBGt/CNBONC7Y3myWTCZJAckXkIp9UrFPAX9ApkBACkgKBEoe/sBxr02BwJG2xxQUCJQsMxCQSVL7Bk2VsPf3oKbkT7lJwXPMTaaSWkwmmUzmY16X1GcqNb1knCXMInNYWEkgeGRdCgSOC6UUKAmijgu6joZZvt/DLKOYIsOlSKtMkeEyhVtlOvo6Mlym8LAj08NligiTKeLI+EirTBHWI8PWI9OPjDsyr7lOtMxxMSUhWFyMzFF0/YDq747XVijtx9+09unLjC4FqHY4vQM4QzkF+fq28IB+aGTSzpgoZTWpo12HLNqVV0v78htIkqwFxbLlFsha5JOl2KdafpNsfinKb1KUT4osDijCX/Js9fkVXuyXtdgvc0AyBUoOqs2STEcPtiU1j4hWbZUcvB5to+CzX+Ziv8IKimUuLJYlv1Dm/CKZ8wtl8hbK5M2XyVsgHcqXOb9IYUU+RXkLFeUtlCkghUWEK6ZRfcUmxKt2QrxiG8Ur0LaF9v+th+onnlUjLwsFgFARFhGu2gkNVDuhQYWuJxAIyFdUdCQg+z00C/iPnBnk98vv88vv8wXH+UtNKwmh/D5fqfbHTgscmff35ZSM8//JOkotp6hYxVl7VZS5W779efLleeU/eFiBQwXyH8ovCZSO3SZJspgki0WmMLNksUhhlpJx4ZaS8RazFGaWzCZJJsl0ZL6jfztNUoTdrsLIkuUHSqaWrOrI8NHt9hf7fn8u9slXfPR1ccl4n0/HR44mWW2RCo+2KTw6SuHRtRRRJ0Z1miWodtOGMlutkqXkzDuZTSWvzWbpj8NHxpmOjJO59DzB4SPjjp1HZtORtuY/rMtyzLQj7+ORgOtoqCVrGMcZAIBKQSAGnIZAIKCX1y/XGz+t1srfdsgX8KtRrRg1j62rhOhYJderr4RasWoUHauEWjFKiI5Vw1oxqmUNV4Slah3o+YqLVXQ4X4f3HdTBXSVntgUfWdnK2bVHu3/equ9efEf+4mLVbtxAiRf21kUP/VO1G1fslykAQPVlMplKLqkMD5dijK6mtIJVm5Tz+Lsq+OZHBQ4ekqlWpMJaJcjirCdLo2MeCXGyNKwrc8N6MtepVXIWUhU6U9rv96s4v0D5OXnKzdpTcpb6kb/hB4J/x7PlyViv/K9zZbVF6azzknT+XTeozYDzjC4fAABDcckkcBpuX/yRnl/7nS5v1V4XndVGf2vaUi3rxFWpoKu85efmKfPbDG1Z8oPWvP1fFRcUavxPnyimQWh3xAgAqF7yv16nPZdOktXpUNT/nafI5I4K79ZaJmvN/T+x3+fTrnX/05YlP+jHD77UL9+v1TVvPKGuKVxiBVR3XDIJnL6a+5cfqEDbD+7VLR176MULhxldSqWJjIlW20Hnq+2g89Xzlqv12sWjtTVthTpffbHRpQEAcMqKNv4qq9OhBt8/XaNDsGOZLRY1SWqvJkntdf5dN+iV/iOV/b9tRpcFAIChuA0PcBp+zT2ocEtoHESfiL1VM8U2jtfHdz+hQ579RpcDAMAp8+8/JN+eAyEThv2R2WxW4aHDOpS91+hSAAAwFIEYgNMy/M0nVXQ4XzN7XKmNn39jdDkAAAAAAJwyAjEAp6VOk4Ya89081TuriV4ddLPmDBujjV98K7/fuNu0AwAAAABwKgjEAJy2+q2ba/SXs3XN3H/LsylTrw68SY+16K/PJ8+Ue/k6+YqKjC4RAAAAAIDjhGbnCQDKjclkUtfrhygp5TK5l6/TitcW6Jtn5uqrh15QRLRNzXonqeX53dUi+Rw17Xa2LFar0SUDAAAAAEIcgRiAcmEymdSsZ2c169lZw16YpB2rf9LWtBXatnSlljz2sj67/2lZbVFq3quLGndpp/qJzVW/zVmqn9hcterXk8lkMnoTAAAAAAAhgkAMQLmzWK3BcOxv946Wr7hYu9a4SgKyb1Zq3Xuf6cAvuxQIBCRJkbVjggGZvXXzkteJzWVv3UwR0bUM3hoAAAAAQE1DIAagwlnCwtS0ewc17d5ByXePkiQV5Rdo71a39mzK1J6N2+XZlKk9mzO18fNvdWjPvuC8tRs3kD2xueytmim2UX1FN7ArpkHcMc9xioiuxRlmAAAAAIBTRiAGwBDWyAg1bN9aDdu3Pm6ad//BkoDsyMOzOVM7Vv2o3N/2KC97n/w+X+llRUUqukGcYhrYS57j4xQdH1d63JHnqDqxhGcAAAAAEOIIxABUOba6teXo0UmOHp2Om+b3+3V4/0Hl7d6r3N2eI897lbfbU/Kc7dHOta7gdF9h6TtdWsKtiqoTq8jYaEXE1FJEbK2S17HRiogueV0yXEsRMb9PO9r+6LTwWjaZzdyoFwAAAACqIwIxANWK2WxWrbi6qhVXVw3atfrTtoFAQPkHc0uCs+x9wdAs/0CuCnIPKT8nT/m5eSrIyVPOzt3Kz8lTQU7J+ILcQyo6nH/SZZtMJoVH234P02JKAjRrZLgsEeEKC7fKEm5VWHh4yXNE6ec/jgsLt/4+37HzR/zF/FYrZ7wBAAAAQBkRiAGosUwmk6LqxCqqTqzi27Qo8/y+oqJgcFbyfEgFOXnHDOcdM+xVQW6eigsKVXy4QPkHcuUrLCoZLiyS7+jzkXHB54LC4M0FTldJ8FbWIC1MJrNZJrNZMplkMptkMplkMplLns2mI+N/HzYdGdYJhs1ms2TSMe3NZVrGiddZskxzsL1ZpiPrODpsPsE6/riMP85z7PCxdVusYX/63hFAAgAAADUHgRgAnITFapWtXh3Z6tWp0PX4iovlO1FYduzzn0z7q/l8BYUqLvo9lCs8dFi+wiIF/H4FAgEF/AEpEJDf75eODJeMP3742Pa/D/ulgI6Z369AQCXPR8b5j5vn+OHj1nns8qsQi9WqsIgTB49/GUaGn8ZZgH88mzAiPHhGYkRMLVnC+FMOAAAAlBVH0QBgMEtYWEmoYYsyupQqKxAoCcxOFqr5j4Ryfwzpjh3+06DP75evqHQw+adn9v1VIHlMAFmQ5/3L4DK4zoLCMr83VlvUkT7ubMf1eRcRE63Io/3kxdQKTvtjH3qRsdGy2qI4+w0AAAAhg0AMAFDllVzOeSSssViMLaYCBQIB+Y+cMXiiswJ9hUUqyi9QQe6hkkdOnvKP6feu4Jh+8Q64dx037c8CN5PZrMjaMYqOr6eYBnGKbmAvuWNrA3twuGRayR1bwwlwAQAAUI0RiAEAUEWYTCZZrFZZrFaF1yr/5RcXFAbDtPyj/d8d0yfe4f05ysveq9zsvcrbvVfZ/9umvN17dWjPvuP6uouItpWEZA3iSkKy+Ljfg7SGdsW1dMjeykFwBgAAgCqJQAwAgBBxtA+yWva6ZZrP7/PpkGe/cnfvDd6tNficXfLsXr5OeUfu6Or3+YLz1nEkqH5ic9lbN1f9xCOPNmepbrMEmWvw2X4AAACo2gjEAADAnzJbLIppYFdMA7ukNn/a1u/3y+vZL8+WX7RnU6Y8mzK1Z3Omtn+zUitf/0/wsk1LuFVxLR3BsCy+zVlq0L6VGie1U1h4eCVsFQAAAEIZgRgAACg3ZrNZ0fFxio6PU/NeSaWm+f1+Hfw1S3s2ZZY8Nm7Xns2Z2rDgc+3P3KlAICBrVKSa9eqiln3PUYu+3eU4p6PCIgjIAAAAUL4IxAAAQKUwm82q26yx6jZrrMQLe5eaVpRfoN82bNLW9BXatnSl0qe9ps8nzVBYZISandtZLfueo87DL1H91s2NKR4AAAA1CoEYAAAwnDUyQk27d1DT7h2UPH6U/D6fdq37n7alr9TW9BVa+tRsfTHlWbU4v5vOGXWlOl5xkaxRkUaXDQAAgGrKbHQBAAAAf2S2WNQkqb3Ov3OkbvjwBU3K+lbD33xSJotF8/5+j55sd7Fcn6YbXSYAAACqKQIxAABQ5VmjIpV03WDduniu7v75U9kTm+v1S2/RvBvuld/vN7o8AAAAVDMEYgAAoFqJb9tCNy96TVe99ohWz/1Q6U++ZnRJAAAAqGboQwwAgBDgdrvl8XiMLqNcWTqdJeeIwfps4tOK6tlW4TG1jC4J1YBt1y5FFRUrIyPD6FIMc+jQIXn2emr0e2C32+VwOIwuAwBQhRGIAQBQw7ndbjmdTnm9XqNLKXe1ZFGCovR0cl8VK2B0OagGWppqqZmplpZ0nWd0KYZpqigV/ORX9vsvGV1KhbHZbHK5XIRiAICTIhADAKCG83g88nq9Sk1NldPpNLocAKhQLpdLKSkp8ng8BGIAgJMiEAMAIEQ4nU4lJSUZXQZqgEAgoAsvvFAWi0Wff/55qWkvvPCC/vWvf+nHH39UkyZNTjh/cnKyOnfurGeeeaYSqgUAADgeneoDAACgTEwmk2bPnq3ly5fr5ZdfDo7fvn27JkyYoGefffakYRgAAEBVQCAGAACAMmvatKlmzJih8ePHa/v27QoEAho1apQGDBigLl26aNCgQYqOjlaDBg10/fXXB2/qMHLkSKWnp2vGjBkymUwymUzKzMw0dmMAAEDIIRADAADAafn73/+ufv366cYbb9Rzzz2nH3/8US+//LL+9re/qUuXLlq1apUWLVqk3bt366qrrpIkzZgxQ+eee65uvvlmZWVlKSsrS02bNjV4SwAAQKihDzEAAACctldeeUXt27fX0qVLtWDBAr388svq0qWLHn300WCb119/XU2bNtWmTZuUmJio8PBw2Ww2NWzY0MDKAQBAKOMMMQAAAJy2+Ph43XLLLXI6nRo6dKjWrVunr7/+WtHR0cFH27ZtJUlbt241uFoAAIASnCEGAACAMxIWFqawsJLDyry8PA0ePFj//ve/j2vXqFGjyi4NAADghAjEAAAAUG6SkpK0YMECNW/ePBiS/VF4eLh8Pl8lVwYAAPA7LpkEAABAufl//+//ad++fRo+fLhWrlyprVu36vPPP9cNN9wQDMGaN2+u5cuXKzMzUx6PR36/3+CqAQBAqCEQAwAAQLlJSEjQsmXL5PP5NGDAAHXo0EF33HGH6tSpI7O55NBz/PjxslgsateunerXry+3221w1QAAINRwySQAADght9stj8djdBmoBi677DJddtllysjICI6bOHGiJk6cWKrdmjVrgq+ff/754Ot9+/Zp3759FV8oqiW73S6Hw2F0GQCAGoZADAAAHMftdsvpdMrr9RpdCoAQZ7PZ5HK5CMUAAOWKQAwAABzH4/HI6/UqNTVVTqfT6HKqnY0bN+raa6/Vm2++qXbt2hldjiFeeeUVLViwQJ9//rnRpRgmJSVF7dq107/+9S+jS6m2XC6XUlJS5PF4CMQAAOWKQAwAAJyU0+lUUlKS0WVUO0f7ymrbtm3Ivn8JCQmyWq0hu/1SyZlNdrs9pN8DAACqKjrVBwAAAAAAQEghEAMAAAAAAEBIIRADAAAAAABASCEQAwAAAAAAQEghEAMAAAAAAEBIIRADAAAAAABASAkzugAAAFBzuN1ueTweo8sw3MaNGyVJ//vf/2Q2h+b/H3ft2qWioiJlZGQYXYphvF6vPB5PSL8Hf2S32+VwOIwuAwAAAjEAAFA+3G63nE6nvF6v0aVUGddff73RJRiua9euRpdgKJfLpQULFhhdRpVhs9nkcrkIxQAAhiMQAwAA5cLj8cjr9So1NVVOp9PoclBGCxcu1PTp05Wenm50KaihXC6XUlJS5PF4CMQAAIYjEAMAAOXK6XQqKSnJ6DJQRuvXr5fFYuFnBwAAQkJodmoBAACASlFUVGTIen0+n/x+vyHrBgAAVR+BGAAAQA318ccfq06dOvL5fJKktWvXymQy6d577w22uemmm5SSkhIc/vzzz+V0OhUdHa2BAwcqKyur1DJfffVVOZ1ORUZGqm3btnrhhReC0zIzM2UymTR//nz17dtXkZGReuutt/5yvl69eumee+4ptZ49e/bIarVq6dKlkqSCggKNHz9ejRs3Vq1atdSjRw+lpaUF28+ZM0d16tTRwoUL1a5dO0VERMjtdp/hOwgAAGoqAjEAAIAaqk+fPsrNzdWaNWskSenp6bLb7aWCpPT0dCUnJ0squSvitGnT9Oabb2rp0qVyu90aP358sO1bb72lSZMm6ZFHHpHL5dKjjz6qBx54QHPnzi213nvvvVf//Oc/5XK5dNFFF/3lfNddd53mzZunQCAQXMb8+fOVkJCgPn36SJLGjBmj77//XvPmzdP69et15ZVXauDAgdq8eXNwHq/Xq3//+9969dVX9dNPPyk+Pr5c308AAFBzEIgBAADUULVr11bnzp2DAVhaWpruvPNOrVmzRnl5edq5c6e2bNmivn37Siq5vPGll15St27dlJSUpDFjxmjx4sXB5U2ePFnTp0/XsGHDdNZZZ2nYsGG688479fLLL5da7x133BFs06hRo7+c76qrrtKuXbv07bffBpfx9ttva/jw4TKZTHK73Zo9e7bee+899enTRy1bttT48eN13nnnafbs2cF5ioqK9MILL6hXr15q06aNbDZbRb21AACgmiMQAwAAqMH69u2rtLQ0BQIBffPNNxo2bJicTqe+/fZbpaenKyEhQa1bt5Yk2Ww2tWzZMjhvo0aNlJ2dLUk6dOiQtm7dqlGjRik6Ojr4ePjhh7V169ZS6+zWrVvw9anMV79+fQ0YMCB4eeX27dv1/fff67rrrpMkbdiwQT6fT4mJiaWWkZ6eXmrd4eHh6tixYwW8iwAAoKbhLpMAAAA1WHJysl5//XWtW7dOVqtVbdu2VXJystLS0rR///7g2WGSZLVaS81rMpmClzHm5eVJkmbNmqUePXqUamexWEoN16pVK/j6VOe77rrrNHbsWD377LN6++231aFDB3Xo0CG4DIvFotWrVx+3rujo6ODrqKgomUymU3hXAABAqCMQAwAAqMGO9iP29NNPB8Ov5ORkPf7449q/f7/GjRt3Sstp0KCBEhIStG3btuCZW+U535AhQzR69GgtWrRIb7/9tkaMGBGc1qVLF/l8PmVnZwf7FAMAADgTBGIAAAA1WN26ddWxY0e99dZbeu655yRJ559/vq666ioVFRWVOkPsrzz44IMaO3asateurYEDB6qgoECrVq3S/v37ddddd53RfLVq1dLQoUP1wAMPyOVyafjw4cH5ExMTdd1112nEiBGaPn26unTpoj179mjx4sXq2LGjLrnkktN8dwAAQKiiDzEAAIAarm/fvvL5fMG7SdarV0/t2rVTw4YN1aZNm1Nezk033aRXX31Vs2fPVocOHdS3b1/NmTNHZ511VrnMd91112ndunXq06ePHA5HqWmzZ8/WiBEjNG7cOLVp00ZDhw7VypUrj2sHAABwKkyBY+9vDeCUdJr7tJKbttSMv11mdCkA8JcyMjLUtWtXrV69WklJSVVqHvw5t9stj8djdBnACdnt9jIFknwWAeXvjtdWKO3H37T2ab6XAGXFJZMAAABVkNvtltPplNfrNboU4IRsNptcLhdn6QEAqiUCMQAAgCrI4/HI6/UqNTVVTqfT6HLOyIoVK/SPf/xDH330kZo0aWJ0OYaYNm2aVqxYoXfffdfoUsqFy+VSSkqKPB4PgRgAoFoiEAMAAKjCnE5ntb/s68CBA5Kks88+Wy1atDC2GIPEx8crMjKy2v8sAQCoKehUHwAAAAAAACGFQAwAAAAAAAAhhUAMAAAAAAAAIYVADAAAAAAAACGFQAwAAAAAAAAhhUAMAAAAAAAAISXM6AIAAABQftxutzwej9FllLJp0yZJ0o8//qgDBw4YW4xBsrOzlZ+fr4yMDKNLOSG73S6Hw2F0GQAAVBoCMQAAgBrC7XbL6XTK6/UaXcoJDRkyxOgSDNe1a1ejSzghm80ml8tFKAYACBkEYgAAADWEx+OR1+tVamqqnE6n0eVUiK5du2ratGm64IILTnsZkydPVm5urp566qmTthk9erQSExM1fvz4015PdeFyuZSSkiKPx0MgBgAIGQRiAAAANYzT6VRSUpLRZVSIrKws1a1bVxEREae9jLi4OFkslj99j6KjoxUfH19j30cAAEIdgRgAAACqjYYNGxpdAgAAqAG4yyQAAACqjOTkZI0dO1YTJkxQvXr11LBhQ02ZMiU43WQy6cMPP5QkZWZmymQy6d1331WfPn0UFRWl7t27a9OmTVq5cqW6deum6OhoDRo0SHv27DluXQ8++KDq16+v2NhY3XrrrSosLDxhTVOnTtXZZ5993PjOnTvrgQceKJftBgAAlYtADAAAAFXK3LlzVatWLS1fvlxPPPGEpk6dqi+//PKk7SdPnqyJEycqIyNDYWFhuvbaazVhwgTNmDFD33zzjbZs2aJJkyaVmmfx4sVyuVxKS0vTO++8o/fff18PPvjgCZd/4403yuVyaeXKlcFxa9as0fr163XDDTeUz0YDAIBKRSAGAACAKqVjx46aPHmyWrdurREjRqhbt25avHjxSduPHz9eF110kZxOp/75z39q9erVeuCBB9S7d2916dJFo0aN0tdff11qnvDwcL3++utq3769LrnkEk2dOlUzZ86U3+8/bvlNmjTRRRddpNmzZwfHzZ49W3379lWLFi3Kb8MBAEClIRADAABAldKxY8dSw40aNVJ2dvYptW/QoIEkqUOHDqXG/XH+Tp06yWazBYfPPfdc5eXl6ddffz3hOm6++Wa98847ys/PV2Fhod5++23deOONp75RAACgSqFTfQAAAFQpVqu11LDJZDrhmVsnam8ymU447s/mPxWDBw9WRESEPvjgA4WHh6uoqEhXXHHFGS0TAAAYh0AMAAAAIWfdunU6fPiwoqKiJEk//PCDoqOj1bRp0xO2DwsL09///nfNnj1b4eHhuuaaa4LzAgCA6odADAAAACGnsLBQo0aN0sSJE5WZmanJkydrzJgxMptP3qPITTfdJKfTKUlatmxZZZUKAAAqAIEYAAAAQk6/fv3UunVrnX/++SooKNDw4cM1ZcqUP52ndevW6tWrl/bt26cePXpUTqEAAKBCEIgBAABDud1ueTweo8soF3a7XQ6Hw+gyyqwq/QyeeuopSVJGRkZw3KRJk4LjVq9eXWr66tWr5ff7g8OxsbFavXq1tm3bFpy/Y8eOWrJkSbDN2LFjg9OGDBkSfP3TTz/9aR2BQEDbt2/XlVdeWWp8VVNd90MAACoTgRgAADCM2+2W0+mU1+s1upRyYbPZ5HK5qlUYUdN+BpXh6aef1tNPP210GSdVHfdDAAAqG4EYAAAwjMfjkdfrVWpqarBvpvK2dOlS3Xnnnfriiy8UFxdXIeuQJJfLpZSUFHk8nmoVRFTGz2DFihX6xz/+oY8++khNmjSpkHVUtK5du6pOnToaP368Bg0aVOb5p02bphUrVujdd9+tgOp+V133QwAAKhuBGAAAMJzT6VRSUlKFLHvXrl2SSi6ba9CgQYWsoyaoyJ/BgQMHJElnn322WrRoUSHrqGiBQOCM5o+Pj1dkZGSFvccAAKBsTn4bHQAAAAAAAKAGIhADAAAAAABASCEQAwAAAAAAQEghEAMAAAAAAEBIIRADAACAIQKBgEaPHq169erJZDJp7dq1f9o+LS1NJpMp2En/nDlzVKdOnQqvsywyMzNPaVsAAICxCMQAAAD+gFCjcixatEhz5szRxx9/rKysLJ199tlGlwQAAEJEmNEFAAAAlIXb7ZbH4znl9tu3b5fNZtOGDRu0c+fOU5pn165dp1teSCjrz2Dr1q2y2Wz66aefgmd3SSVnfMXFxSkyMlK7du36y/d906ZNkqR169YpJiZGmZmZ8vl8ysjIOK3tqAhHt8Hlcsnv9wfHH93ustZqt9vlcDjKrT4AAFCCQAwAAFR5H3/8sVJSUrRmzRqdffbZ8nq9ZV7GhRdeWOZ5unTpIknq27ev0tLSyjx/TeR2u+V0Ok/rZ3DZZZedcHzXrl3LtJzk5OQzmr8yXHvttSccX9ZabTabXC4XoRgAAOWMQAwAAFR5ffr0UW5urpYtWyav16uUlBR9/PHHatKkiebOnStJGjp0qP7+97+rW7duGj58uG677Tadd9552r9/v/79738rMTFRU6ZMkSRdeuml8nq9uvXWW3Xuuefqq6++0gsvvKD33ntPzZs318cff6zJkyfrxRdf1NChQxUeHm7g1lctHo9HXq9Xqampcjqdp72c3NxczZs3Tx988IHeeOMNWSwWvfrqq1q8eLEeeOABNWrUSHPnztXSpUv14Ycfqnbt2lq1apVuueUWpaWlKSYmRgsXLtT06dOVnp4uSVqyZInuu+8+jRs3Tj169NA333yjmTNn6vnnn1f37t21dOlSPfDAA1qyZIksFovGjRuntWvXasiQIRo7dqyys7M1aNAgffjhh2ratKk+/fRTzZgxQ/fcc4/atGmjjRs36uGHH9add96pwYMHy+v16rLLLlOPHj104403aufOnZo2bZp27typt99+W23atDmj99rlciklJUUej4dADACAckYgBgAAqrzatWurc+fOWrVqlSRpx44duvvuu/Xggw8qMTFRBw8e1K+//qoRI0bo3//+t66//npNmzYtOH/r1q3Vt29fvfvuu4qMjFR4eLj69eunxx9/XFLJmUvvvvuusrOzNWzYsOBlb7Vr11bDhg0rf4OrAafTqaSkpDNaxpo1axQZGakBAwbo0KFDGjRokObMmRM8u2rIkCFq3ry5Vq1apbvvvls5OTmSpE6dOqlOnTpav369LBZLsI7bb79dN9xwQ/Dnevnll2vHjh1auHChbrnlFrVo0ULjxo2TyWRSly5dtH79et1zzz368MMPlZSUpLfeekuNGzfWkCFDJElXXXWVZs6cqeHDh0uSBg8erPz8fH366aeaPHmyXnnlFZnNZn3wwQeKjIyUJEVGRuof//iHnE6nOnfufEbvDwAAqDh0qg8AAKqFvn37avXq1ZJKgpRhw4bJ6XTq22+/VXp6uhISEtS6dWutW7dOc+bMUXR0dPBx0UUXye/3a/v27cHldezYMfjaZDKpYcOGys7OrvTtQomtW7eqqKhIvXv3Do6zWq0655xz5HK5TmkZLper1PyS1Lt37+D8derUUadOnZSWlqYNGzYoPDxco0eP1po1a5SXl6f09HT17dtXknTo0CFt3bpVo0aNKrUvPfzww9q6dWtwfR07dgyGYZJ07rnnntH7AAAAKgdniAEAgGohOTlZs2bNkiSFhYWpbdu2Sk5OVlpamvbv3x8MMvLy8nTLLbdo7Nixxy3j2MvOrFZrqWkmk6lUJ+iomY7uMxEREerbt6/q1atXKlgdN26cpJL9SJJmzZqlHj16lFqGxWKp9LoBAED5IhADAADVQp8+fYIdubdu3VoZGRlq2rSp5syZo9zcXKWkpCgjI0PNmzfX8uXLg5fXHevHH3+UJBUWFmrHjh2l7vh3+PBhZWVlKSMjQ5mZmZJEQPYnTvWsrT+zY8cOFRYWKiMjQ4cPH5bValVqaqoGDRokSSoqKtJ3332n4cOHKyMj4y/vMulwOPTRRx+pQ4cOwXV8/PHHaty4cbBNkyZNNGvWLOXl5alXr17KyMhQu3bt9Nxzz2nTpk2qX79+sG39+vX17bffnrCvtP3796tWrVrKyMjQ999/r4iICEnSggULgu/Pme4/5fEeAwCAEyMQAwAA1ULdunXVtm1b/fzzz/rhhx+Ou1vfI488okceeSQ4/Fd385s+fbqmT59eatymTZv0yiuvBIfXrl2r/v37KzIyUrVr1y6Hraj+7Ha7bDabUlJSym2Zx/6sJk6cqIkTJ5aaPmPGDM2YMSM4/Gd3mVy3bp0++OCDP12HJKWnpys9PV2PPfZYqfHDhg0rNfzSSy/ppZde+tP6e/Xqddy4k91lsqxsNpvsdnu5LAsAAPyOQAwAAFQbF154oX7++Wf95z//0VlnnSVJGj58uPbu3asvvvgi2O6nn37S888/rw0bNqi4uFiFhYW64YYbNGbMGEkld5m89tprS4UWw4cPV3Jysm655RZJ0pdffqnnn39eTz31lPr06aO0tLTK29AqzOFwyOVyyePxnPI8K1as0D/+8Q999NFHatKkSXD822+/rbffflsff/yxJKmgoEAzZszQ559/Lq/XK6fTqXHjxql9+/aS9Jd3mZSk9957T2+++aZ2796txo0ba9SoUbrkkktK1fPHfebgwYPq16+fBgwYoEcffbRU288++0xvvvmmtm3bpqioKLVq1UrDhw/X3/72N0nShg0b9Oijj2r79u1q0aKFbrrpJt19993H3WVy2rRpWrFihd59991Tft+kkgCSO0wCAFD+CMQAAEC18cwzz+iZZ54pNW7jxo3HtUtKStL1118vqeSSucGDB+uxxx5TgwYNJCl4F8k/W05SUpLuueeecqq8ZnE4HGUKaQ4cOCBJOvvss9WiRYvg+KSkpFJ3A5X+vFP6pKQkjR49utTwlClTjmvzx7O+/uhE+8zJLm9MSkrS/fff/6c1/f3vfy81bvz48ce1i4+PV2Rk5BnfmRMAAJQP7jIJAAAAAACAkEIgBgAAAAAAgJBCIAYAAAAAAICQQiAGAAAAAACAkEIgBgAAAAAAgJBCIAYAAAAAAICQEmZ0AQAAAC6Xq8KWvWXLFknS+vXrFRcXV2HrqchtqAwVWf+mTZskST/++KMOHDhQYeupyrKzs5Wfn6+MjIwKXU913w8BAKgsBGIAAMAwdrtdNptNKSkpFb6uAQMGVPg6bDab7HZ7ha+nPFXmz2DIkCEVvo6qrmvXrhW+juq4HwIAUNkIxAAAgGEcDodcLpc8Ho/RpZQLu90uh8NhdBllUtN+Bqie+yEAAJWNQAwAABjK4XDw5d1g/AwAAECooVN9AAAAAAAAhBQCMQAAAAAAAIQUAjEAAAAAAACEFAIxAAAAAAAAhBQCMQAAAAAAAIQUAjEAAAAAAACEFAIxAAAAAAAAhBQCMQAAAAAAAIQUAjEAAAAAAACElDCjCwAAAED5crlcRpeAaoT9BQAQigjEAAAAagj7/2/v3oPsvMvDjj/v+55zdvfo4pV0LFuyfIJvYCtcjB1oQgPExgmCQJKhYTrjimknTCcwnbaZgXZi0z867SRkGpM005m0SaZpJyR0ypSkJE2EG2IcSBswYFzssNixMVi2ZN2vez2Xt3+cs6s9q5XxRdIr7e/zmdl57+c851haa796d9VqRbPZjN27d1c9CpeZZrMZrVar6jEA4KIRxAAA1oh2ux1TU1Nx+PDhqkcZ8dBDD8WHP/zh+OxnPxs7duyoepxK3HffffHQQw/Fpz/96apHWVWr1Yp2u131GABw0QhiAABrSLvdvuTCxvHjxyMi4rWvfW1cf/311Q5Tka1bt8b4+HjcdtttVY8CAIQfqg8AAABAYgQxAAAAAJIiiAEAAACQFEEMAAAAgKQIYgAAAAAkRRADAAAAICm1qgcAAODcpqamqh7hFXviiSciIuKxxx6L48ePVztMRQ4ePBhzc3Px8MMPVz3KebEWfl0CkDZBDADgEtRqtaLZbMbu3burHuW8+emf/umqR6jc7bffXvUI502z2YxWq1X1GADwsghiAACXoHa7HVNTU3H48OGqR4FVtVqtaLfbVY8BAC+LIAYAcIlqt9uCAwDABeCH6gMAAACQFEEMAAAAgKQIYgAAAAAkRRADAAAAICmCGAAAAABJEcQAAAAASIogBgAAAEBSBDEAAAAAkiKIAQAAAJCUWtUDAABry9TUVNUjAJcgnxsAuJQIYgDAedFqtaLZbMbu3burHgW4RDWbzWi1WlWPAQCCGABwfrTb7ZiamorDhw9XPUrlHn/88bj77rvjk5/8ZOzcubPqcSrx27/92/GZz3wm7r///qpHqczu3btj586dce+991Y9yiWj1WpFu92uegwAEMQAgPOn3W77Yjci8nzwY1pvvvnmuO222yqephrbt2+Per2e7OuPOHM3VMrvAQBcqvxQfQAAAACSIogBAAAAkBRBDAAAAICkCGIAAAAAJEUQAwAAACApghgAAAAASalVPQAAcOmampqqeoTL0uOPPx4REd/+9rcjz9P8+8d9+/ZFp9OJhx9+uOpRKjMzMxOHDx9O+j14pXwOAuBCEcQAgLO0Wq1oNpuxe/fuqke5rH3gAx+oeoTK3X777VWPUKmpqan4zGc+U/UYl7VmsxmtVqvqMQBYYwQxAOAs7XY7pqam4vDhw1WPAiSu1WpFu92uegwA1hhBDABYVbvd9kUoAABrUpo/1AIAAACAZAliAAAAACRFEAMAAAAgKYIYAAAAAEkRxAAAAABIiiAGAAAAQFIEMQAAAACSIogBAAAAkBRBDAAAAICk1KoeAAC4OKampqoeAeCC87kOgBdDEAOANa7VakWz2Yzdu3dXPUoytkQjfjZ2xB/Gc3Eo5qsepxIfLK6L9+XXxE92/qrqUSrzvrgmDsV8fCkOVz1KcprNZrRararHAOASJogBwBrXbrdjamoqDh/2RfnFcuzx78bn/sG98Qe/9/uxZef1VY9TiebvPhjj//Nr8fU//nrVo1Tm/g98LDbfcn38+3s/WPUoyWm1WtFut6seA4BLmCAGAAlot9u+OLyInssn4nMRcfPNr4lrb3td1eNU4sSex+N0vR633XZb1aNU5kvDu5RSfg8A4FLlh+oDAAAAkBRBDAAAAICkCGIAAAAAJEUQAwAAACApghgAAAAASRHEAAAAAEiKIAYAAABAUgQxAAAAAJIiiAEAAACQFEEMAAAAgKQIYgAAAAAkRRADAAAAICmCGAAAAABJEcQAAAAASIogBgAAAEBSBDEAAAAAkiKIAQAAAJAUQQwAAACApAhiAAAAACRFEAMAAAAgKYIYAAAAAEkRxAAAAABIiiAGL9PR2Zkoy7LqMQC4BM2dOFX1CAAAvIBa1QPA5eif3PqW+NDn/zA++9TfxLUbJqO9cTJ2bLgi2hsm49oNk7F5vBnr6o1YV69Hs94Yrg8+mrV6FLkWDXA5KMsyunPzsTA9GwvTM7EwMzdYTs/GwvRszJ+ajhPPHYgTe5+PY3v3xfFn9sfxvc/H6QOH4zW73hpXvvq6ql9CZeo3XxtZoxanf2dPNN//1sgn11c90kUzf3o6vvXHD8T00eOx7Q2vqXocAGAVghi8DB983Zvimg0b4/Gjh+KZU8dj78nj8cjBffEnT03FwZnT3/f68VotmrVBMFu3SjBbV2+MhLRmrR6NoohaXkQ9z6OW51HLi8Eyy6NeFFHLFvfnUV88tnJ7eM73Oz/PBDvg4ijLMvq9XvQ73eh1u9Hv9qLf7UWv01la73e70et0l9YHx7uD/S/inOXHO7PzI0GrMzu7tL4UvaZnY2FmLjrDAFb2+y/4GurNidjU3haT7W2x/Q23xM733BGT7W1xy7vfHuMb04lAK42/8/YY+9zX4tg//c049s//U9TfeH2Mv/31Ub/52si3bY5i+JFv3hBZllU97ktWlmXMnTgVJ/cdjBP7DsbJfQfj8BPfjScf/ErsfejR6He7sfOn7ow3vP9dVY8KAKwiK33PF5xXc91OnJifi+nOwtLHTLczsj3d6Qz3Lzun0zl7fdnxbr8fnX4vuv1+9C/wb9sssqgX+bJodnaIqy+uL0a25ZHuHCGuyPPIIiLPsojIIs+yyLLB8w3Ws8hjsC/PsshWrI8sR66L4XXZ0uNn2YrzI5btO3PO8uOj1614/CxfcV2ce56R6879Os7ed+bxR/97rPjvs+x4tuLo8ktf0rFzPH6v243e/EL05jvRXViIstePsiyj7JcRUUbWLwfbZRlFtxd5GaPv3eJrKiPqRRFjRS3G8iIaRXFmoGW/ns/6X9KK7ZHjZ526/Ni5r1vtOcoyBtGjLKPsD15jlBH9kX0xcrxcfO2L5yy+L6ucs3LfmfP7q14XKx5/cZ4z5/dH51u2b+U5o88zui/KMvov9Nzl6L5zxqhO50WEqd5Zcarf7UW/14sLrajXI68VkdeKqE+MR2PdRDTWTUS9ObG03ljXjMbidnMi6utWHFt35tjI/vXNGN+4/rIMOhdL9+nnY+4vH435B78Z8196NHrPHRk9oVEbxrEtUWzbFPnGdZFNNAYfY8Pl+JllOVaLfp5FL4/oZVmUEVEufjqJMsrszKeHXpTRL8vBOWUZkWWDc4a/D/pRRrffi15ZRqfXjd5CJzqz89GZnYvu3PxgfW4+urNz0Zmdj7lTp+PU/kNxYt/BOLX/UHRm50ZeyvqtW+L6t/1Q3HDHD8dN7/jhaN30Kr82gAvqF/7zQ/HgY8/HI7/+U1WPApcdd4jBeTZeq8d4rX5Bn6Nf9qPXL5cCWbffj27Zj06vF92yf2ZfvxedZeuDqDZcH57XWXZseXQbeczh+qrnLNvulWcfn+ksRGe4r78YHpa+QCmX9i1ul2VEPwZfjI/sW35+DLeX1vsj1y0/PnJ+rL7vrLmG67w0H/yNL8bks8eqHuOyk+X5ILLmeUQWy9azyPMsYjHCDvdl+Yrt1fYNtwePMXzc4fPE8LxsuG/knBd8jCyKem0YlmpR1GtRGx+LvDbYV9RrkRfD9eE5eb12Zn3xnFptKU4tD1WLj7m0/iLOWfmYxSrPufS6qUztuqtj/XVXx/p/9OMREdGfW4jT3/5enHrsOzHzxN5Y+N7z0Xn2cJQHD0b25NORz3cj7/Yj7/Uj75VRlBFFP6KIwV9+vBT/tzkTXzv+3Eu6JosY/EVOlkeRF4O7qmu1qBVF1MfGYsOmK+Lq7dfGxjf/UFxx3Y6YfHU7rth5fUzuvC4a65ov6bkAgOoIYnAZyrM88mJwxw0XTnmOaLf8boNzRrVYGeVWi32xItCdffffyiw3cqfTimO9fj+OPPW92PvQY/HMV78Zzz/6tzFz9Njw3MEXkUWtFs0rN8W6KzfFus2TUWuOR63RiNpYPYpGI4rxRtTGGlE06lEbG4tirBa1scZgvV6LrCgi8hgJKxGDeNL4rXdEv3fmNfRXvIedsh/zvW4sdHsx3+/FfK8X873uYF+/F3PD9U5vsH5ifi4OzU7HodnpmO4sLHvdWTRr9bhuclO86ep2vHnbtXHr1u0xUauP3Aa3soGMRJGVd+EtRanRALXavuVx6UxkOjts5SPBapUotcrdgLAWnT54JJ78wlfiyQe+HN/54lfj6NPPRm+hM3LOuis3x8btW2Pj66+M8Ss2RH1iLOrj41Ebb0R9Yjxq42ODfY1GNIraIFLlRdTzIvJld+VGWUYM7+jNIuLNeRY/1O8PPgMOP2lmw3tkszIiizJqZRZFGVEbxrdsvhsx14mYW4j+3ELE3EKUs4OP/qmZ6D1/NHrfOxr9r/5NRPlYRETMR8SBWhHFjlaM/egPxviPvSHGfuz1UdvRumjvMwDw0ghiAOdw5lsqq57k+zvwrSfjU7v/Rex7ZCqKRj1e9ZY3xg//zE/GplddM/gic/vW2LjtymhumRyEmsvM6YX52D99KvZPn4x9p0/Gc6dPxjcOPBef3PtU/Oq3/jaaT9Tj1+54b/zj171ZZIJLRGd2Lv7ko/8u/vo/fioiIrbeckPceOePxFU7b4iN265c+ty04epW1BqNiqd96cpON3oHT0Rv/5Ho7z8avf1Ho/PEczH/xUdj5vcfiIiI5vvfFpv+w4eT+gcFAOByIYgBrAF/+Wv/JRZmZuODf/Y7ccPb3xT1ifGqRzqv1jfG4qbGWNy0afRui7IsY+rowfilLz8Qv/a1L8U7X/Xq+IGNmyqaElhu6k//Mh77o/8d7/nVfxlvvPs9sXHb1qpHOq+yei1q12yJ2jVbzjrWO3wiZj/75Tjxb/8gpv/7F2PDz7+7ggkBgBdy+d0mAMBZnv7S12Lne++Mm3e9dc3FsBeSZVns3HJV/Ku/c2c8cexQ7D15vOqRgKED33oyIiLe/pGfW3Mx7PspWlfE+g++M/Jtm6Pz6NNVjwMArEIQA1gjUv5WwZRfO1zKUv+9mfrrB4BLmSAGAAAAQFIEMQAAAACSIogBAAAAkBRBDAAAAICkCGIAAAAAJEUQAwAAACApghgAAAAASRHEAAAAAEiKIAYAAABAUgQxAAAAAJIiiAEAAACQFEEMAAAAgKQIYgAAAAAkRRADAAAAICmCGAAAAABJEcQAAAAASIogBgAAAEBSBDEAAAAAkiKIAQAAAJAUQQwAAACApAhiAAAAACRFEAMAAAAgKYIYAAAAAEkRxAC47OVZFhvqY/Gl556OQzOnqx4HklaWZRz5zt7Y/+gTMbZhXdXjAACsqlb1AADwSr1685Wx67pXx8f+6v742F/dH9duuCJu23pNbFu/MTaPN2PLRHOwXLE+OT4etbyoeny4LHTnF2Lm6PGYOXI8Zo6eiOkjx2N2uFzcd/S7z8ZzD38rZo+diIiI9/3mv652aACAcxDEAFgT/tt77o5fOn40vn7gufj6gefim4f2xZf3PRNH5qbj6NxsTHcWVr1ucmwiNo9PxJaJZjRrjRgrihgrajFeq8VYUYvGsvXBRxHjtfpZ5y1+LN8+s372uUXuJm1eurIso9/tRnd+YfAxt3BmfX4hunPzqx7rzS9EZ3hscb033xm9bmH5YwyOzZ+ajpmjJ2LmyPFYmJ5ZdaaJyY0xsfmKWLdlMjZec1W89Rf+Yey4/Qdjx+0/GBuual3kdwgA4MURxABYE/Isjxs3teLGTa34+ze/4azj891uHJ2biSNzM3FkdmawPjvYXlyf63ZivteNuV43js3Nxnyvt7Q93+2eWR9uz/W60S/LlzVvLc+jkRdR5HnkWRZFtrjMRrbzLFt2Trbi3GXnZFnkWR5Fvsr1Kx87zyOPM4+7eCw712PnWeQxOB4RkcVwOVhE2etHv9uNstuLXrcbZXew3e/2lvb3u93odXvR7wy2y7KMKMuIMobL4fvYL6N9qhvloeMRZUSWZZGV5XA5eM48sqjledSyPOp5HkWWRT0vosiyqGWD/bU8X3ZOMZx4EJSGK0vbZa8fZb8f/V4vyn45XB/u6w/39Xoj+8t+OTy/P3j9w+Xg2LLre8uvX3bNsvWlx1z1+sVjvej3+tGdX4iy339Zv+aKej1qY/UoxhpRG2tEbXxssFzaPrM+sWlj1MYa0WhORHPLZDS3TMa6LZPR3HzF0nZz82RMbNoYRc0fJwGAy48/wQCQhLFaLbat3xjb1m88r4/b7fcG4Wx5MFslns33ejHX64ycOz8Mar2yHC77g2W/P7J/5NjSOWX0o4x+2R+sl4P1M9f1R6/v96Nb9qPXe5GPvbjd78f83Hx0Op0zUavbi35vsOz1eoPYs1jHhsqlzeysfVmWRWTZ6DnLLn/vX38vrnjkuyPXLGbHs5YrnqccHWPxCaPI8yjyM6FssF5ELc9jrF6P8UYjsjyPvCgiy7PI8jyyIh/ZN1jmy9azyIoi8lot6kvn5sNzBtfnWR7ZyPXZ6PMUReQjz3Xm+qX1ZXOsDFcvFLWKsUbUh8eLsUbk7koEAFgiiAHAKzCIKkWsqzeqHuW8Obn/YHzjU/8rnvzCV+LpL3415k8PvlWuqNdjw7YrY+P2K2Pj9q2x8erhctuVMbZhXdQnxqM2Phb1ibHhcnx0fXwsikZ96U6zV6Jf9mO+O4iMs91uzHY7MdcdrA+Ww/VeJ47Nzcb+6VOx7/TJeGb6ZOw7Pfg4ODMd5TCtbVu3Ie5s3xh3tm+Iu29+Y4y56wkAYE3zpz0AYMlTD34lfu9n/1kszMzFdT96e9x5z8/H9T/25mjd+APR3DJ5ydxllGd5TNTzmKjXY9PLfIxOrxcHZk7FY4cPxAPPPBVf2PtkfGrqkfiNh/9P/Nn7fi62n+e7CQEAuHQIYgDAkicffCh23P7auPtT98W6LS83NV0e6kUROzZMxo4Nk7HrutdERMQjB/fFh/78D+Oh/c/Ez9z02oonBADgQrk0/poXALgkHPibv43IszUfw87l1q3b4xsH98W+6VNVjwIAwAUkiAEAAACQFEEMAAAAgKQIYgAAAAAkRRADAAAAICmCGAAAAABJEcQAAAAASIogBgAAAEBSBDEAAAAAkiKIAQAAAJAUQQwAAACApAhiAAAAACRFEAMAAAAgKYIYAAAAAEkRxAAAAABIiiAGAAAAQFIEMQAAAACSIogBAAAAkBRBDAAAAICkCGIAAAAAJEUQAwAAACApghgAAAAASRHEAAAAAEhKreoBAOBiOHLkSBw7diw2b94cmzdvrnocAACgQu4QA2BN27NnT9x1113RarXipptuii1btsRdd90Vn/vc56oeDQAAqIggBrAGXP+2N8WzX38spv70wejMzVc9ziXj4x//eLz73e+OBx98cGT/gw8+GO9617viV37lV6oZ7BI0d/J0/L9P74mT+w5F+02vr3ocAAC4oHzLJMAa8Jp3/mjs+divx+++90NRG2vE5LXb4oprr47Ja6+OyR2L69ti8tqrY+O2rdFYNxG18bHIsqzq0S+YPXv2xL333hsREb1eb+TY4vY999wTt956a+zateuiz3exlGUZndm5mD81HSf3HYzje5+P43v3x4m9z8fxZ/ef2X72QPS73dh+6y1x04+/peqxAQDgghLEANaA1//srnjd33tnHPz2d+LJv/jrOPa9fXH82efj8JPPxFNfeChO7jsY/RVRKMvzaKybiLH1zWisa0Z9aX0iGsN9jeayfeuaw/1n9hX1euS1IrKiiKJWRF4rIq/VIi/ywXJxX7Hs2Gr78vN/w/J9990XRVGcFcOWK4oiPvGJT5z3IFaWZZT9fvR7veh3Fz+6g+Wyfb1uN8peP/rdbvSG55S9/tJ6v9uL3kInFqZnYmF6NuZPD5YL0zOxMFyfn15l33DZGR4ry3L0ddfrccWOqwbBtL0tXvWWN8bm63bEq3/i78bmV+04r+8FAABcigQxgDUiy7K46pYb4qpbbjjrWK/bjVPPH47je/fH6QNHhtFkehBSlkWW+emZ6EzPxsL0bEwfOjYSWRZDS7/bvSCzZ8NAVgwjWTaMaouhLcvziCwbnLt4Z1s2uDaG24vrM71OPPD4A9/3eXu9Xnz+85+Pf3PzT8REUY8oyzPxaHF9aXNxu4x+rx9lrzcSrkZj1/l/jyIiikZ9ECZXiZcTkxtj8pqrR4PmyHnrYsPVrZhsb4v1W7dckAgJAACXC0EMIAFFrRaTO66OyR1Xv+LH6i4sLEWyxbuclu6AGt7tNHJX1LJ9y++COnNX1Iu7tt/rRZSxFKwW49RqAWv/8SMRj//5i35NV/3I62Pb5JZzBrZsZDtWveOtWCXinX233HC5Yl9RK1ZcW1u6i24QwSaW7sgDAABeOUEMgJek1mhEbXMjmpsnqx7lnI4cORIf+q+feNHnv/8T98bmzZsv4EQAAMClxPdLALDmbNmyJd7xjndEURQveF5RFHHXXXeJYQAAkBhBDIA16aMf/egL/kD9iMHPEPvIRz5ykSYCAAAuFYIYAGvSrl274pd/+ZcjIs66U2xx++Mf//h5/xcmAQCAS58gBsCadc8998SePXvijjvuGNl/xx13xJ49e+IXf/EXK5oMAACokh+qD8CatmvXrti1a1d89Y/uj99634fjnoc/Gze88XVVjwUAAFTIHWIAJGFy48bYlDVi0+SmqkcBAAAqJogBAAAAkBRBDAAAA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-<pre> sample -&gt; 16, 
- But Pennington to whom he appealed shook his head . Too big a risk Hally replied Pen . And trebly dangerous with that greaser Darrin in the class .
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-<pre> sample -&gt; 17, 
- unk the three middies stepped inside the darkened shed . Suddenly the door banged and a padlock clicked outside . Here stop that you rascally joker roared Dalzell wheeling about .
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-<pre> sample -&gt; 18, 
- We 're sure enough idiots groaned Dan . Hold on there fellows interrupted Dave Darrin . unk all your anger right on me .
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-<pre> sample -&gt; 19, 
- No retorted Dave shaking his head . We 've seen the uselessness of asking help from outside . Let 's supply our own help .
-Skipping line 20 due to an error: Bobcat failed to parse "We ' ve seen the uselessness of asking help from outside .".
- sample -&gt; 20, 
- Farley took hold at the further end . Up with it heaved Midshipman Darrin . Right up the spar went .
-</pre>
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"/>
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-<pre> sample -&gt; 21, 
- It 's all up with our summer 's fun . Is it though shouted Dave spurting ahead . Come on and find out .
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-<pre> sample -&gt; 22, 
- The blamed thing is water logged he uttered disgustedly . The hands point to less than half past nine . Darrin managed to get at his own watch .
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-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre> sample -&gt; 23, 
- I 'm growing terribly hungry . Catch a fish smiled back Darrin . And eat it raw gasped Dalzell .
-Skipping line 24 due to an error: Bobcat failed to parse "I ' m growing terribly hungry .".
- sample -&gt; 24, 
- What then is your slang for a pretty girl . Oh we call her a queen . And a girl who is who is n't pretty .
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<pre> sample -&gt; 25, 
- And for being a bit worse than touge . Which did they call you demanded Belle . Dave started then sat up straight staring at Miss Meade .
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"/>
-</div>
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-<pre> sample -&gt; 26, 
- So I 'm a gold brick am I frowned Belle . You a gold brick stammered Dave . Why you oh go look in the glass .
-Skipping line 27 due to an error: Bobcat failed to parse "So I ' m a gold brick am I frowned Belle .".
- sample -&gt; 27, 
- Yet something in her eyes gave the midshipman hope . Belle he continued eagerly do n't trifle with me . Tell me will you marry me some day .
-</pre>
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"/>
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-<pre> sample -&gt; 28, 
- I 'm not going to reach 2 . 5 in some studies this month groaned Dan . I know that much by way of advance information .
-Skipping line 29 due to an error: Bobcat failed to parse "I ' m not going to reach 2 .".
- sample -&gt; 29, 
- Here 's the very proposition . The others crowded about nodding . I remember that one laughed Freeman lightly .
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kycODEvv/xyuxNbly5dss022/zL+wKdxxZbbNH2WSCDBg1Kkrz22muZPn16Ro8enbXWWitJ/u0x5Z81NjbmL3/5S7p169b2P7bLqqamJnvvvXf23nvvXHDBBRk0aFB+8pOf5Iwzzliu9QBQHf/u3LIsGhsbM2fOnGy55ZYdOCkrqrGxMTfddFMGDx6cbt2WzgDbbbdd1l577bS0tGT//fdfadt9/vnnM2fOnFx77bXZd999kyT33nvvSls/rAhvmWSN1dLSkjPOOCNz5szJDTfckK997Ws5/fTT33b5pqam3HXXXbn44ovz2GOPZdKkSbnqqqsyZsyYdstNmTIll112WR577LF8/etfz49+9KO29R588MHZYYcdctxxx2XmzJmZNm1ahg8fnv333z+77rpr/v73v2fUqFGZPHly/vjHP2bKlCmZPn16W1D7Z4MHD86TTz6ZWbNmpbW1Na+88spSyxx33HHp0aNHTjjhhDz00ENtv/Xnfe97XzbaaKN3+uUD1jC9evXKqaeemrPOOiu33XZbHnnkkXz84x/PwoULc9JJJ2XQoEGpqanJz372szz33HNZsGDBMq334IMPzp577pkjjjgid9xxR5566qncd999Oe+889p+Q+5bmTp1aj7/+c/n/vvvT0tLS26++eY899xzb3u8A2D18+/OLcviggsuyPe+972MGzcuDz/8cJqbm3PjjTfms5/9bAdPz/L45Cc/mRdeeCHHHHNMpk+fnrlz5+b222/PyJEjs3jx4vTp0ydjxozJpz/96UyaNClz587NzJkz87WvfS2TJk16x9tdd911s/766+eaa67JE088kV//+tf+44zVhleIscYaPnx4/v73v2f33XdP165dc/rpp+eUU0552+UbGhry3//937ngggty8cUXp3///rnooosyYsSIdsudeeaZuf/++zNu3Lj07ds3X/7yl9t+VXFNTU1++tOf5lOf+lT222+/dOnSJYccckjbb1Hp2rVrnn/++QwfPjzPPPNM6urq8qEPfSjjxo17y5k+/OEPt/164RdffDETJkxYap7a2trcfvvtOf3007Pbbru1vWT5nHPOeYdfOWBNdemll2bJkiU5/vjj87e//S277rprbr/99qy77rpZd911M27cuJx77rkZOXJkhg8fvky/abampia/+MUvct5552XkyJF57rnnsvHGG2e//fb7l9G9b9+++d///d9cccUV+etf/5pBgwZl/PjxOfTQQ1fiIwago/2rc8uyGDZsWH72s5/loosuyhe/+MV079492267bU4++eQOnpzlsckmm2TKlCk555xz8t73vjevvPJKBg0alEMOOSRdurz+OpmLL744G2ywQb7whS/kD3/4Q/r165fGxsb813/91zvebpcuXXLjjTfmtNNOy/bbb59tttkmX/3qVzN06NCV9MjgnaupVPx+dNY8Q4cOzU477ZQrrrji3y47c+bM7LLLLpkxY0YaGxv/5bKDBw/O6NGjl/kl4tWwPI8HWDN1pn/nnemxwOqsM/1b60yPZVXpTF+zzvRYVpXO9jXrbI+H1Ze3TAIAAABQFEEMAAAAgKL4DDHWSJMnT+6Q9T711FMdsl4AAABg9eEVYgAAAAAURRADAAAAoCjeMkkxmpubqz3CStFZHgfw73WGf++d4THAmqQz/JvrDI+hWjrD164zPIZq6Sxfu87yOFj9CWJ0enV1damtrU1TU1OHbmdg/w9nnT7b5aHHLu7Q7SRJbW1t6urqOnw7QHWsquPWKde1ZOqNn8/sn32zQ7fjmAUdb1UcN4Zu3j0faFg7Y36+oMO28QbHjeWzKr7/63bvmlsbt8k5c1py34sduw/4/i+fVfW8odthE1J5+dks/vU5HbqdxD7AqlFTqVQq1R4COlpLS0taW1s7dBs3/fD/8sjDC3L+RVt36HaS10969fX1Hb4doHpWxXHrv1t3yI61f8m2tc916HYcs2DV6OjjxrPTf5x593wnQ8b8ssO28QbHjeXX4eeNv76Urp89PYtPGZ1sv1PHbSe+/+/Eqnje8Mnb18kGtUsydt+/deh2EvsAq4ZXiFGE+vr6Dj+g/ubuxXly7qI0NjZ26HaAMqyK49ZNv1qSTTfdNI2DB3bodoBVo6OPGw/Nm5pnunTxXGc11dHf/9deeD6PJNlyiy3S1z6w2lkVzxv6THk2663TNY2NW3XodmBV8aH6AAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCjdqj3A6qClpSWtra3VHoM1XM/av2WrbdfKzJkzqz0KnURdXV3q6+s7dBuOf2Ub2H3T/O0vL2XmC3+r9ihUyao4ziSONZ3FX//eJes0HOC5Tqn+vjA1u++dJ55/IbEPFGm7vj3Tp3slM2f+sdqjsBKsqucAq7OaSqVSqfYQ1dTS0pKGhoYsXLiw2qMAtFNbW5vm5uYOO1E5/gEdfZxJHGsAYHW0Kp4DrO6Kf4VYa2trFi5cmOuuuy4NDQ3VHgcgSdLc3Jympqa0trZ22EnK8Q/KtiqOM4ljDQCsblbVc4DVXfFB7A0NDQ1pbGys9hgAq5zjH7AqONYAAKsTH6oPAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAUYOzYsdlpp53+5TIjRozIEUcc0XZ56NChGT16dIfOBUDnN3ny5NTU1OTFF1+s9igAbbpVewBYk40YMSKDBw/O2LFjqz0KwEp38803p3v37tUeAwAAVjpBDAB4S+utt161RwDgLbz66qtZa621qj1G1fk6ACvCWyZhJfnGN76RrbbaKj169MhGG22Uj3zkI9UeCVhDDR06NKeddlrOPvvsrLfeetl4443bvRK1paUlhx9+eHr37p2+ffvmox/9aJ555pl267j00kuz0UYbpU+fPjnppJPyj3/8o93tixcvzhlnnJF+/fpl/fXXz9lnn51KpbLUHG9+y+TgwYPz+c9/PieeeGL69OmT+vr6XHPNNe3uc99992WnnXZKjx49suuuu+aWW25JTU1NZs2atVK+NgAlGjp0aEaNGpXRo0enrq4uw4YNy0MPPZRDDz00vXv3zkYbbZTjjz8+ra2t7e7zqU99KqNHj866666bjTbaKNdee21efvnljBw5Mn369MmWW26ZX/7yl+221VHrTZIpU6Zkxx13TI8ePbLHHnvkoYceanf7vffem3333Tc9e/bMwIEDc9ppp+Xll19uu33w4MG5+OKLM3z48PTt2zennHLKyvoSAwUSxGAluP/++3Paaafloosuypw5c3Lbbbdlv/32q/ZYwBps0qRJ6dWrV6ZOnZrLLrssF110Ue68884sWbIkhx9+eF544YXcc889ufPOO/OHP/whRx99dNt9//u//ztjx47N5z//+dx///3p379/vvGNb7Rb//jx4zNx4sR897vfzb333psXXnghP/nJT/7tXOPHj8+uu+6a3//+9/nEJz6RU089NXPmzEmS/PWvf81hhx2WHXbYITNnzszFF1+cc845Z+V+YQAKNWnSpKy11lqZMmVKLr300hx44IHZeeedc//99+e2227LM888k49+9KNL3aeuri7Tpk3Lpz71qZx66qk56qijstdee2XmzJl573vfm+OPPz4LFy5Mkrz44osdst43nHXWWRk/fnymT5+eDTbYIIcddlhee+21JMncuXNzyCGH5MMf/nAeeOCB/PCHP8y9996bUaNGtVvH5ZdfniFDhuT3v/99zj///JX9ZQZKUincjBkzKkkqM2bMqPYorMFuuummSt++fSt//etfqz0KncSqODY5/q2+9t9//8o+++zT7rrddtutcs4551TuuOOOSteuXSstLS1ttz388MOVJJVp06ZVKpVKZc8996x84hOfaHf/d7/73ZUhQ4a0Xe7fv3/lsssua7v82muvVTbddNPK4Ycf3m6O008/ve3yoEGDKk1NTW2XlyxZUtlwww0rV199daVSqVSuvvrqyvrrr1/5+9//3rbMtddeW0lS+f3vf7/cXwc61qo6BjjWwIrbf//9KzvvvHPb5Ysvvrjy3ve+t90yTz/9dCVJZc6cOW33efO5ZNGiRZVevXpVjj/++Lbr5s2bV0lS+e1vf9uh67377rsrSSo33nhj2zLPP/98pWfPnpUf/vCHlUqlUjnppJMqp5xySrtt/+Y3v6l06dKl7bwyaNCgyhFHHLFMXzPg7Tk3v84rxGAleM973pNBgwZl8803z/HHH5/rr79+qf8RA1geO+64Y7vL/fv3z7PPPpvm5uYMHDgwAwcObLttu+22S79+/dLc3JwkaW5uzrvf/e52999zzz3b/v7SSy9l3rx57Zbp1q1bdt111+Waq6amJhtvvHGeffbZJMmcOXPa3grzht13331ZHi4A/8Yuu+zS9vfZs2fn7rvvTu/evdv+bLvttklef6XVG958zO7atWvWX3/97LDDDm3XbbTRRknSdhzvqPW+4c3novXWWy/bbLNN27lr9uzZmThxYrttDxs2LEuWLMmTTz7Zdr9lOVcBLAsfqg8rQZ8+fTJz5sxMnjw5d9xxRy644IKMHTs206dPT79+/ao9HrAG+uff7lhTU5MlS5ZUaZr/Z3WdC6Cz69WrV9vfFyxYkMMOOyxf/OIXl1quf//+bX9/q2P2m6+rqalJkrbjeEetd1ksWLAg//Ef/5HTTjttqdvq6+vb/v7mrwPAivAKMVhJunXrloMPPjiXXXZZHnjggTz11FP59a9/Xe2xgE6moaEhTz/9dJ5++um26x555JG8+OKL2W677dqWmTp1arv7/e53v2v7+zrrrJP+/fu3W2bRokWZMWPGCs22zTbb5MEHH8wrr7zSdt306dNXaJ0ALK2xsTEPP/xwBg8enC233LLdnxUJRh213je8+Vw0f/78PPbYY2loaGjb9iOPPLLUdrfccku/SRLoEIIYrAQ/+9nP8tWvfjWzZs3KH//4x3zve9/LkiVLss0221R7NKCTOfjgg7PDDjvkuOOOy8yZMzNt2rQMHz48+++/f9vbSE4//fR897vfzYQJE/LYY4/lwgsvzMMPP9xuPaeffnouvfTS3HLLLXn00UfziU98Ii+++OIKzXbsscdmyZIlOeWUU9Lc3Jzbb789l19+eZL/92oBAFbcJz/5ybzwwgs55phjMn369MydOze33357Ro4cmcWLF692633DRRddlLvuuisPPfRQRowYkbq6uhxxxBFJknPOOSf33XdfRo0alVmzZuXxxx/PT3/606U+VB9gZRHEYCXo169fbr755hx44IFpaGjIN7/5zdxwww1517veVe3RgE6mpqYmP/3pT7Puuutmv/32y8EHH5zNN988P/zhD9uWOfroo3P++efn7LPPzi677JI//vGPOfXUU9ut58wzz8zxxx+fE044IXvuuWf69OmTI488coVm69u3b/7nf/4ns2bNyk477ZTzzjsvF1xwQZK0+1wxAFbMJptskilTpmTx4sV573vfmx122CGjR49Ov3790qXLO/8Rr6PW+4ZLL700p59+enbZZZf85S9/yf/8z/+0vfprxx13zD333JPHHnss++67b3beeedccMEF2WSTTVZ4uwBvpaZSqVSqPUQ1zZw5M7vssktmzJiRxsbGao8DkGTVHJsc/1gVrr/++owcOTIvvfRSevbsWe1xeJNVdQxwrAGA1Ytz8+t8qD4AsNJ873vfy+abb54BAwZk9uzZOeecc/LRj35UDAMAYLUiiAEAK81f/vKXXHDBBfnLX/6S/v3756ijjsoll1xS7bEAAKAdQQwAWGnOPvvsnH322dUeAwAA/iUfqg8AAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABSlW7UHKEVLS0taW1urPQZvoa6uLvX19dUeYyn2mdXX6rrPrM7sz7B8HGeWn+MMAJ2F5wGrhiC2CrS0tKShoSELFy6s9ii8hdra2jQ3N69WBxz7zOptddxnVmf2Z1h+jjPLx3EGgM7E84BVQxBbBVpbW7Nw4cJcd911aWhoqPY4K6SlpSVHHnlkrrnmmuyyyy7VHmeFNTc3p6mpKa2travVwaYz7TNJsueee2b06NE5+uijqz3KCltd95nVWWfbnzuTfffdN6eeemqOPfbYao/CmzjOLD/HGVaFadOm5dRTT82tt96aAQMGVHscquCggw5KU1NTRo4cWe1R6MQ8D1h1BLFVqKGhIY2NjdUeY4X06dMnSbL11luv8Y9lTdAZ9pkkqampycCBAzvFY+Gd6yz7c2fSpUuXbLrppr4vdBqOM3Sk+fPnJ0m23377bLbZZlWehmro1q1bBgwY4DgDnYQP1QcAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICidKv2AKxcLS0taW1t7dD1J8ljjz2WPn36dNh2kqSuri719fUdug06fp9JkkqlkqeffjozZ87s0O3YZ1gV+3NnsmTJkvzpT3/q8H+bnY1jTdkcZ8r1+OOPJ0keeuihzJ8/v8rTUA2LFi3Kn//8Z+fNgnkO0LkIYp1IS0tLGhoasnDhwg7f1imnnNLh26itrU1zc7MDTgdalfvMZZddlssuu6xDt2GfKduq3J87k/Hjx2f8+PHVHmON4lhTLscZkuSDH/xgtUegiq666qpcddVV1R6DKvEcoHMRxDqR1tbWLFy4MNddd10aGhqqPc4KaW5uTlNTU1pbWx1sOpB9hs6kM+3PrL4ca8rmOANQLs8BOh9BrBNqaGhIY2NjtcdgDWKfoTOxPwMdzXEGANZ8PlQfAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQKNWLEiBxxxBFLXT958uTU1NTkxRdfXOUzAQAAAKwKghgr1auvvlrtEVjDvPbaa9UeAQAAgMIIYvxL9957b/bdd9/07NkzAwcOzGmnnZaXX3657fbBgwfn4osvzvDhw9O3b9+ccsopy3Q/1kzXXHNNNtlkkyxZsqTd9YcffnhOPPHEJMlPf/rTNDY2pkePHtl8880zbty4LFq0qG3ZmpqaXH311fngBz+YXr165XOf+1y23HLLXH755e3WOWvWrNTU1OSJJ57o+AcGAABAUQQx3tbcuXNzyCGH5MMf/nAeeOCB/PCHP8y9996bUaNGtVvu8ssvz5AhQ/L73/8+559//jLfjzXPUUcdleeffz53331323UvvPBCbrvtthx33HH5zW9+k+HDh+f000/PI488km9961uZOHFiLrnkknbrGTt2bI488sg8+OCDOemkk3LiiSdmwoQJ7ZaZMGFC9ttvv2y55Zar5LEBAABQDkGsYD/72c/Su3fvdn8OPfTQttu/8IUv5Ljjjsvo0aOz1VZbZa+99spXv/rVfO9738s//vGPtuUOPPDAnHnmmdliiy2yxRZbLPP9WPOsu+66OfTQQ/ODH/yg7bof//jHqaurywEHHJBx48bl3HPPzQknnJDNN98873nPe3LxxRfnW9/6Vrv1HHvssRk5cmQ233zz1NfXZ8SIEZkzZ06mTZuW5PW3Uf7gBz9oe9UZAAAArEyCWMEOOOCAzJo1q92fb3/72223z549OxMnTmwXzIYNG5YlS5bkySefbFtu1113bbfeZb0fa6bjjjsuN910U1555ZUkyfXXX5+Pfexj6dKlS2bPnp2LLrqo3ff+4x//eObNm5eFCxe2reOf95lNNtkk73//+/Pd7343SfI///M/eeWVV3LUUUetugcGAABAMbpVewCqp1evXku9He1Pf/pT298XLFiQ//iP/8hpp5221H3r6+vbrefNlvV+rJkOO+ywVCqV/PznP89uu+2W3/zmN/nKV76S5PXv/bhx4/KhD31oqfv16NGj7e//vM8kycknn5zjjz8+X/nKVzJhwoQcffTRqa2t7bgHAgAAQLEEMd5WY2NjHnnkkeX+DKd3ej/WDD169MiHPvShXH/99XniiSeyzTbbpLGxMcnr3/s5c+a8o+/9+973vvTq1StXX311brvttvzv//7vyh4dAAAAkghi/AvnnHNO9thjj4waNSonn3xyevXqlUceeSR33nlnrrrqqpV+P9Ycxx13XD7wgQ/k4YcfTlNTU9v1F1xwQT7wgQ+kvr4+H/nIR9reRvnQQw/lc5/73L9cZ9euXTNixIh85jOfyVZbbZU999yzox8GAAAAhfIZYrytHXfcMffcc08ee+yx7Lvvvtl5551zwQUXZJNNNumQ+7HmOPDAA7Peeutlzpw5OfbYY9uuHzZsWH72s5/ljjvuyG677ZY99tgjX/nKVzJo0KBlWu9JJ52UV199NSNHjuyo0QEAAMArxEo1ceLEt7x+6NChqVQqbZd322233HHHHW+7nqeeeuotr/9392PN1qVLl/zf//3fW942bNiwDBs27G3v++b965/9+c9/Tvfu3TN8+PAVnhEAAADejleIAVX3yiuv5E9/+lPGjh2bo446KhtttFG1R4I11lNPPZWamprMmjWr2qMAsIaZPHlyampq8uKLL1Z7FIAOJ4gBVXfDDTdk0KBBefHFF3PZZZdVexxgGdXU1OSWW26p9hgAvENDhw7N6NGjV/p6Bw8enCuuuGKlrxdgZRLEgKobMWJEFi9enBkzZmTAgAHVHgcAAIBOThADgDXMbbfdln322Sf9+vXL+uuvnw984AOZO3duu2UeffTR7LXXXunRo0e233773HPPPe1uv+eee7L77rtn7bXXTv/+/XPuuedm0aJFbbe/1f/u77TTThk7dmzb7Uly5JFHpqampu0yAGuGESNG5J577smVV16Zmpqa1NTUtH0+8IwZM7LrrrumtrY2e+21V+bMmdN2v7lz5+bwww/PRhttlN69e2e33XbLr371q7bbhw4dmj/+8Y/59Kc/3bZegNWRIAYAa5iXX345Z5xxRu6///7cdddd6dKlS4488sgsWbKkbZmzzjorZ555Zn7/+99nzz33zGGHHZbnn38+yeu/wOJ973tfdtttt8yePTtXX311vvOd7+Rzn/vcMs8wffr0JMmECRMyb968tssArBmuvPLK7Lnnnvn4xz+eefPmZd68eRk4cGCS5Lzzzsv48eNz//33p1u3bjnxxBPb7rdgwYK8733vy1133ZXf//73OeSQQ3LYYYelpaUlSXLzzTdn0003zUUXXdS2XoDVkd8y2Qk1Nzd32Lpfe+21vPDCC1l33XWz1lprddh2OvIxsLSO/no/88wz6dOnT2praztsG/YZSvLhD3+43eXvfve72WCDDfLII4+kd+/eSZJRo0a1LXf11Vfntttuy3e+852cffbZ+cY3vpGBAwfmqquuSk1NTbbddtv83//9X84555xccMEF6dLl3/9/2QYbbJAk6devXzbeeOOV/AgB6GjrrLNO1lprrdTW1rYdxx999NEkySWXXJL9998/SXLuuefm/e9/f/7xj3+kR48eGTJkSIYMGdK2nosvvjg/+clPcuutt2bUqFFZb7310rVr1/Tp08f5AVitCWKdSF1dXWpra9PU1FTtUVaK2tra1NXVVXuMTs0+A2umxx9/PBdccEGmTp2a1tbWtleGtbS0ZLvttkuS7Lnnnm3Ld+vWLbvuumtbOG5ubs6ee+7Z7m0se++9dxYsWJA//elPqa+vX4WPBoDVzY477tj29/79+ydJnn322dTX12fBggUZO3Zsfv7zn2fevHlZtGhR/v73v7e9QgxgTSGIdSL19fVpbm5Oa2trh22jpaUlRx55ZK655prssssuHbad5PVY44eyjrUq9pnk9R/MR48enaOPPrpDt2OfoRSHHXZYBg0alGuvvTabbLJJlixZku233z6vvvrqSttGly5dUqlU2l332muvrbT1A7D66t69e9vf3/jPkzf+82XMmDG58847c/nll2fLLbdMz54985GPfGSlnoMAVgVBrJOpr6/v0CDQp0+fJMnWW2+dxsbGDtsOq05H7zPJ60+kBg4caJ+BleD555/PnDlzcu2112bfffdNktx7771LLfe73/0u++23X5Jk0aJFmTFjRkaNGpUkaWhoyE033ZRKpdL2g86UKVPSp0+fbLrppklef0vkmz/35a9//WuefPLJdtvo3r17Fi9evPIfJACrxFprrbXcx/EpU6ZkxIgROfLII5O8/plib3wY/4qsF2BV86H6ALAGWXfddbP++uvnmmuuyRNPPJFf//rXOeOMM5Za7utf/3p+8pOf5NFHH80nP/nJzJ8/v+1DkT/xiU/k6aefzqc+9ak8+uij+elPf5oLL7wwZ5xxRtvnhx144IH5/ve/n9/85jd58MEHc8IJJ6Rr167ttjF48ODcdddd+ctf/pL58+d3/IMHYKUaPHhwpk6dmqeeeqrdW/D/la222io333xzZs2aldmzZ+fYY49d6n6DBw/O//7v/+bPf/5zh78TAeCdEsQAYA3SpUuX3HjjjZkxY0a23377fPrTn86XvvSlpZa79NJLc+mll2bIkCG59957c+utt7Z9xt6AAQPyi1/8ItOmTcuQIUPyn//5nznppJPy2c9+tu3+n/nMZ7L//vvnAx/4QN7//vfniCOOyBZbbNFuG+PHj8+dd96ZgQMHZuedd+7YBw7ASjdmzJh07do12223XTbYYINl+hywL3/5y1l33XWz11575bDDDsuwYcOWehfARRddlKeeeipbbLFF2y9hAVjdeMskAKxhDj744DzyyCPtrnvz53298fdjjjnmbdex//77Z9q0aW97e9++fXPjjTe2u+6EE05od/mwww7LYYcdtsxzA7B62XrrrfPb3/623XUjRoxod3mnnXZqd44ZPHhwfv3rX7db5pOf/GS7y3vssUdmz569cocFWMm8QgwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQlG7VHgAA1iQtLS1pbW2t9hi8hbq6utTX11d7DFgpHGsAlp3nALwTghgALKOWlpY0NDRk4cKF1R6Ft1BbW5vm5mZPiFnjOdYALB/PAXgnBDEAWEatra1ZuHBhrrvuujQ0NFR7nBW277775tRTT82xxx5b7VFWWHNzc5qamtLa2urJMGu8znas6SymTZuWU089NbfeemsGDBhQ7XGogoMOOihNTU0ZOXJktUfhTTwH4J0SxABgOTU0NKSxsbHaY6ywLl26ZNNNN+0UjwU6o85yrOks5s+fnyTZfvvts9lmm1V5GqqhW7duGTBggH+X0En4UH0AAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFG6VXuAkjQ3N1d7hBXW0tKSbt265fHHH0+fPn2qPc4KW92/J6v7fMuqa9eu+dOf/pSZM2dWe5QV1lm+JwAAACUTxFaBurq61NbWpqmpqdqjrDQf//jHqz3CSlNbW5u6urpqj9FOZ9xnvvjFL+aLX/xitcdYKVbHfQYAAIBlJ4itAvX19Wlubk5ra2u1R+Et1NXVpb6+vtpjtGOfWb2tjvsMAAAAy04QW0Xq6+v9AM1ysc8AAABAx/Ch+gAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARelW7QEAgKW1tLSktbW1Q7exZMmS/OlPf8rMmTM7dDt1dXWpr6/v0G0Ay29VHGc6k8cffzxJ8tBDD2X+/PlVnoZqWLRoUf785z93+Hmzs/E8gNWVIAYAq5mWlpY0NDRk4cKFHb6t8ePHZ/z48R26jdra2jQ3N3syDKuRVXmc6Ww++MEPVnsEquiqq67KVVddVe0x1iieB7C6EsQAYDXT2tqahQsX5rrrrktDQ0O1x1khzc3NaWpqSmtrqyfCsBrpTMcZYPXleQCrM0EMAFZTDQ0NaWxsrPYYQCfmOANAqXyoPgAAAABFEcQAAAAAKIogBgAAAEBRBDEA4F8aOnRoRo8evULruPXWW9OvX7+VMg8AAKwoQQwAAACAoghiALCGeu2116o9AgAArJEEMQBYjSxZsiSTJk1Kkuyxxx6pr6/PJZdckqeeeio1NTX54Q9/mP333z89evTI9ddfnyVLluSiiy7KpptumrXXXjs77bRTbrvttrb1feQjH8moUaPaLo8ePTo1NTV59NFHkySvvvpqevXqlV/96ldJkpdffjnDhw9P7969079//4wfP36pGV955ZWMGTMmAwYMSK9evfLud787kydPbrfMxIkTU19fn7322itJ8tJLL63UrxMAAKwIQQwAViOf+cxnMnHixCTJj370o/zgBz/IRhtt1Hb7ueeem9NPPz3Nzc0ZNmxYrrzyyowfPz6XX355HnjggQwbNiwf/OAH8/jjjydJ9t9//3ax6p577kldXV3bddOnT89rr73WFq7OOuus3HPPPfnpT3+aO+64I5MnT87MmTPbzThq1Kj89re/zY033pgHHnggRx11VA455JC2bU6dOjUnnXRSRo0alRtuuCFJ8p3vfKcjvlwAAPCOCGIAsJr429/+liuvvDKnnXZakmTgwIHZZ599cvLJJ7ctM3r06HzoQx/KZpttlv79++fyyy/POeeck4997GPZZptt8sUvfjE77bRTrrjiiiSvfyD+I488kueeey7z58/PI488ktNPP70tiE2ePDm77bZbamtrs2DBgnznO9/J5ZdfnoMOOig77LBDJk2alEWLFrVtv6WlJRMmTMiPfvSj7Lvvvtliiy0yZsyY7LPPPpkwYUKS5Morr8whhxySs88+O4MGDUqS7LnnnqvgKwgAAMumW7UHAABe19zcnFdeeSW777772y6z6667tv39r3/9a/7v//4ve++9d7tl9t5778yePTtJsv3222e99dbLPffck7XWWis777xzPvCBD+TrX/96ktdfMTZ06NAkydy5c/Pqq6/m3e9+d9u61ltvvWyzzTZtlx988MEsXrw4W2+9dbttvvLKK1l//fXbHseRRx7Z7vYddtghU6dOXdYvBQAAdChBDABWEz179vy3y/Tq1Wu51llTU5P99tsvkydPztprr52hQ4dmxx13zCuvvJKHHnoo9913X8aMGbPM61uwYEG6du2aGTNmpGvXru1u692793LNBgAA1eItkwCwmthqq63Ss2fPTJs2bZmW79u3bzbZZJNMmTKl3fVTpkzJdttt13b5jc8Rmzx5coYOHZouXbpkv/32y5e+9KW88sorba8w22KLLdK9e/d2r+SaP39+HnvssbbLO++8cxYvXpxnn302W265Zbs/G2+8cZKkoaFhqVeDPfTQQ8v3xQAAgA7kFWIAsJro0aNHzjnnnHz5y19Okjz99NN59dVX8/DDD+eggw56y/ucddZZufDCC7PFFltkp512yoQJEzJr1qxcf/31bcsMHTo0n/70p7PWWmtln332abtuzJgx2W233dpedda7d++cdNJJOeuss7L++utnww03zHnnnZcuXf7f/59tvfXWOe644zJ8+PCMHz8+O++8c5577rncdddd2XHHHfP+978/p512Wvbee+9cfvnl2XLLLZMk9913X7v1AABANQliALAaOf/88/Pss8/mG9/4Rj7ykY9kk002yX/+53++7fKnnXZaXnrppZx55pl59tlns9122+XWW2/NVltt1bbMDjvskH79+mXrrbdue1vj0KFDs3jx4rbPD3vDl770pSxYsCCHHXZY+vTpkzPPPDMvvfRSu2UmTJiQz33ucznzzDPz5z//OXV1ddljjz3ygQ98IEmyxx575Nprr82FF16Y5557Lkly0kkntX3oPgAAVJsgBgCrkS5duuSkk07KN77xjUydOjWNjY1tt1Uqlbdc/sILL8yFF174L9f5wgsvtLtup512esv19e7dO9///vfz/e9/v+26s846q90y3bt3z7hx4zJu3Li33eaJJ56YE088MTNnzswuu+yS448/Pl/5ylfednkAAFiVvHcBAACgExo6dGhGjx6dJBk8eHCuuOKKZb7vU089lZqamsyaNWu5t7u82wKoBkEMAACgk5s+fXpOOeWUlbrOiRMnpl+/fit1nQCrirdMAgAAdHIbbLBBtUcAWK14hRgAAEAn989vY3z00Uezzz77pEePHtluu+3yq1/9KjU1Nbnlllva3e8Pf/hDDjjggNTW1mbIkCH57W9/mySZPHlyRo4cmZdeeik1NTWpqanJ2LFjl9ruiSee2PZLV97w2muvZcMNN8x3vvOdlf0wAZaZIAYAAFCQxYsX54gjjkhtbW2mTp2aa665Juedd95bLnveeedlzJgxmTVrVrbeeuscc8wxWbRoUfbaa69cccUV6du3b+bNm5d58+ZlzJgxS93/5JNPzm233ZZ58+a1Xfezn/0sCxcuzNFHH91hjxHg3/GWyf9fc3NztUdgDdf63KtZ+PLi1A/uWe1R6AQck0g6fj+YPXt2+vfvnw033LDDtmFfBlj93HnnnZk7d24mT56cjTfeOElyySWX5D3vec9Sy44ZMybvf//7kyTjxo3Lu971rjzxxBPZdttts84666SmpqZtHW9lr732yjbbbJPvf//7Ofvss5MkEyZMyFFHHZXevXt3wKMDWDbFB7G6urrU1tamqamp2qOwhhs04GNZp88OeeDRt/7fNVhetbW1qaurq/YYVEFnOzfZlwFWL3PmzMnAgQPbhazdd9/9LZfdcccd2/7ev3//JMmzzz6bbbfddpm3d/LJJ+eaa67J2WefnWeeeSa//OUv8+tf//odTg+wchQfxOrr69Pc3JzW1tZqj8Ia7qYf/l8eeXhBJlw/o9qj0EnU1dWlvr6+2mNQBavq3LTvvvvm1FNPzbHHHtuh27EvA6y5unfv3vb3mpqaJMmSJUuWax3Dhw/Pueeem9/+9re57777stlmm2XfffddqXMCLK/ig1jy+g8enqizon5z9+I8OXdRGhsbqz0K0AmsinNTly5dsummmzpuARRmm222ydNPP51nnnkmG220UZJk+vTpy72etdZaK4sXL/63y62//vo54ogjMmHChPz2t7/NyJEjl3tbACubD9UHAAAoyHve855sscUWOeGEE/LAAw9kypQp+exnP5vk/70KbFkMHjw4CxYsyF133ZXW1tYsXLjwbZc9+eSTM2nSpDQ3N+eEE05Y4ccAsKIEMQAAgIJ07do1t9xySxYsWJDddtstJ598cttvmezRo8cyr2evvfbKf/7nf+boo4/OBhtskMsuu+xtlz344IPTv3//DBs2LJtssskKPwaAFeUtkwAAAJ3Q5MmT2/7+1FNPtbtt2223zb333tt2ecqUKUmSLbfcMsnrr/6qVCrt7tOvX7+lrrv66qtz9dVXt7vun7eVJC+//HLmz5+fk046aXkfBkCHEMQAAAAK85Of/CS9e/fOVlttlSeeeCKnn3569t5772yxxRYrdTtLlixJa2trxo8fn379+uWDH/zgSl0/wDsliAEAABTmb3/7W84555y0tLSkrq4uBx98cMaPH7/St9PS0pLNNtssm266aSZOnJhu3fwICqweHI0AAAAKM3z48AwfPrzDt/NWb70EWB34UH0AAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAo3ao9AAAA0Hm1tLSktbW12mMA/6Suri719fXVHgOqRhADAAA6REtLSxoaGrJw4cJqjwL8k9ra2jQ3N4tiFEsQAwAAOkRra2sWLlyY6667Lg0NDdUeZ4VMmzYtp556am699dYMGDCg2uNQBQcddFCampoycuTIao+ywpqbm9PU1JTW1lZBjGIJYgAAQIdqaGhIY2NjtcdYIfPnz0+SbL/99tlss82qPA3V0K1btwwYMGCN35eB1/lQfQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUbpVewAAWNM0NzdXe4SVYsCAAXnppZcyc+bMao+ywjrL9wQAgFVDEAOAZVRXV5fa2to0NTVVe5SVZuzYsRk7dmy1x1gpamtrU1dXV+0xAABYAwhiALCM6uvr09zcnNbW1mqPwluoq6tLfX19tccAAGANIIgBwHKor68XXQAAYA3nQ/UBAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIrSrdoDAAAArKiWlpa0trZ22Poff/zxJMlDDz2U+fPnd9h2WH0tWrQof/7znzNz5swO3U5dXV3q6+s7dBuAIAYAAKzhWlpa0tDQkIULF3b4tj74wQ92+DZYfV111VW56qqrOnQbtbW1aW5uFsWggwliAADAGq21tTULFy7Mddddl4aGhmqPA+9Yc3Nzmpqa0traKohBBxPEAACATqGhoSGNjY3VHgOANYAP1QcAAACgKIIYAAAAAEURxAAAAAAoiiAGAACwBhs6dGhGjx69Src5YsSIHHHEESt9vRMnTky/fv1W+noB/pkgBgAAAEBRBDEAAAAAiiKIAQAAdBLf//73s+uuu6ZPnz7ZeOONc+yxx+bZZ59tu/2t3pJ4yy23pKampt11n/vc57LhhhumT58+Ofnkk3Puuedmp512Wmp7l19+efr375/1118/n/zkJ/Paa6+13fbKK69kzJgxGTBgQHr16pV3v/vdmTx5crv7T5w4MfX19amtrc2RRx6Z559/foW/BgDLQhADAADoJF577bVcfPHFmT17dm655ZY89dRTGTFixHKt4/rrr88ll1ySL37xi5kxY0bq6+tz9dVXL7Xc3Xffnblz5+buu+/OpEmTMnHixEycOLHt9lGjRuW3v/1tbrzxxjzwwAM56qijcsghh+Txxx9PkkydOjUnnXRSRo0alVmzZuWAAw7I5z73uRV5+ADLrFu1BwAAAGDlOPHEE9v+vvnmm+erX/1qdttttyxYsCC9e/depnV87Wtfy0knnZSRI0cmSS644ILccccdWbBgQbvl1l133Vx11VXp2rVrtt1227z//e/PXXfdlY9//ONpaWnJhAkT0tLSkk022SRJMmbMmNx2222ZMGFCPv/5z+fKK6/MIYcckrPPPjtJsvXWW+e+++7LbbfdtjK+FAD/kleIAQAAdBIzZszIYYcdlvr6+vTp0yf7779/kqSlpWWZ1zFnzpzsvvvu7a7758tJ8q53vStdu3Ztu9y/f/+2t2c++OCDWbx4cbbeeuv07t277c8999yTuXPnJkmam5vz7ne/u90699xzz2WeE2BFeIUYAABAJ/Dyyy9n2LBhGTZsWK6//vpssMEGaWlpybBhw/Lqq68mSbp06ZJKpdLufm/+3K/l0b1793aXa2pqsmTJkiTJggUL0rVr18yYMaNdNEuyzK9UA+hIghgAAEAn8Oijj+b555/PpZdemoEDByZJ7r///nbLbLDBBvnb3/6Wl19+Ob169UqSzJo1q90y22yzTaZPn57hw4e3XTd9+vTlmmXnnXfO4sWL8+yzz2bfffd9y2UaGhoyderUdtf97ne/W67tALxT3jIJAADQCdTX12ettdbK1772tfzhD3/IrbfemosvvrjdMu9+97tTW1ub//qv/8rcuXPzgx/8oN0H4SfJpz71qXznO9/JpEmT8vjjj+dzn/tcHnjggaV+E+W/svXWW+e4447L8OHDc/PNN+fJJ5/MtGnT8oUvfCE///nPkySnnXZabrvttlx++eV5/PHHc9VVV/n8MGCVEcQAAAA6gQ022CATJ07Mj370o2y33Xa59NJLc/nll7dbZr311st1112XX/ziF9lhhx1yww03ZOzYse2WOe644/KZz3wmY8aMSWNjY5588smMGDEiPXr0WK55JkyYkOHDh+fMM8/MNttskyOOOCLTp09PfX19kmSPPfbItddemyuvvDJDhgzJHXfckc9+9rMr9DUAWFbeMgkAALAGmzx5ctvfjznmmBxzzDHtbv/nzww74ogjcsQRR7S77uMf/3i7y+eff37OP//8tsvvec97suWWW7Zd/udXlSXJFVdc0e5y9+7dM27cuIwbN+5tZz/xxBPb/WbMJDnzzDPfdnmAlUUQAwAAoM3ChQvzzW9+M8OGDUvXrl1zww035Fe/+lXuvPPOao8GsNIIYgAAALSpqanJL37xi1xyySX5xz/+kW222SY33XRTDj744GqPBrDSCGIAAAC06dmzZ371q19VewyADuVD9QEAAAAoiiAGAAAAQFG8ZRKATqW5ubnaI6wxfvnLX2abbbbJ5ptvXu1R1hj2L1i9deS/0eeeey5Tp07NAQcckF69enXYdlh93XHHHdlss82y1VZbddg2nGdg1RHEAOgU6urqUltbm6ampmqPQidXW1uburq6ao8BvMmqPAdceOGFHb4NyuY8A6uGIAZAp1BfX5/m5ua0trZWe5Q1xr777ptTTz01xx57bLVHWaPU1dWlvr6+2mMAb7IqzgHTpk3LqaeemltvvTUDBgzosO2w+jrooIPS1NSUkSNHduh2nGdg1RDEAOg06uvrPYFcDl26dMmmm26axsbGao8CsMI6+hwwf/78JMn222+fzTbbrMO2w+qrW7duGTBggPMmdBI+VB8AAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACK0q3aAwBQhubm5mqPwD/Zdddds2jRosycObPao/Am/q3QGXWG/fqZZ57Jnnvumccffzzz58+v9jhUwc4775wkneK82Rn+TcKKEsQA6FB1dXWpra1NU1NTtUfhLUyePLnaI/AWamtrU1dXV+0xYIV1xnPAsGHDqj0CVXTnnXfmM5/5TLXHWCmcayidIAZAh6qvr09zc3NaW1urPQqsMerq6lJfX1/tMWCFOQfA6su5htIJYgB0uPr6ek+4AArlHADA6siH6gMAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFKVbtQcAoLqam5urPQJV8vDCDbNR9wWp676w2qNQBf7tk9gPlsef/vSn/OIXv8jHPvax9O3bt9rjUAXf+973ssMOO2TnnXeu9ihrDMcYVmeCGECh6urqUltbm6ampmqPQpV86uYXM+V7X8rMW75a7VGoktra2tTV1VV7DKrAOeCd+9a3vlXtEWCN4lzD6koQAyhUfX19mpub09raWu1RqJL/bq3NmWeOybbnn1DtUaiSurq61NfXV3sMqsA5YPlNmzYtp556am699dYMGDCg2uNQBQcddFCampoycuTIao+yRnGuYXUliAEUrL6+3hOUgt30qyXZdNNN0zh4YLVHAarAOWD5zJ8/P0my/fbbZ7PNNqvyNFRDt27dMmDAgDQ2NlZ7FGAl8KH6AAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUJRu1R4AAABYWnNzc7VH4E1efPHFfPjDH84f/vCHzJ8/v9rjUAWHHnpoevfunZkzZ1Z7FN7EsZJ3ShADAIDVSF1dXWpra9PU1FTtUXgLN910U7VHoIq+//3vV3sE3kJtbW3q6uqqPQZrGEEMAABWI/X19Wlubk5ra2u1RwFYI9TV1aW+vr7aY7CGEcQAAGA1U19f74c7AOhAPlQfAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFKVbtQcAAIA1SXNzc7VHAGAVc+zvfAQxAABYBnV1damtrU1TU1O1RwGgCmpra1NXV1ftMVhJBDEAAFgG9fX1aW5uTmtra7VHoQqmTZuWU089NbfeemsGDBhQ7XGogoMOOihNTU0ZOXJktUehSurq6lJfX1/tMVhJBDEAAFhG9fX1fhgq1Pz585Mk22+/fTbbbLMqT0M1dOvWLQMGDEhjY2O1RwFWAh+qDwAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEXpVu0BAABgZWhubq72CHRijz/+eJLkoYceyvz586s8DdWwaNGi/PnPf87MmTOrPQqdmHPZqiOIAQCwRqurq0ttbW2ampqqPQoF+OAHP1jtEaiiq666KldddVW1x6CTq62tTV1dXbXH6PQEMQAA1mj19fVpbm5Oa2trtUcBgBVWV1eX+vr6ao/R6QliAACs8err6/3wAAAsMx+qDwAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQlG7VHgAAgM6vubm52iMAAHFOfoMgBgBAh6mrq0ttbW2ampqqPQoraJcB3bLvZmvlinsXVnsUqqBvt6758rb1+dofn8nsv9kHStR13wtT+ccLWTL9a9UehZWgtrY2dXV11R6jqgQxAAA6TH19fZqbm9Pa2lrtUVhBz07/cebd853MmPGbao9CNfz1pXT97On57lWfTbbfqdrTUAWfvH2dbFC7JGO/OaLao7AS1NXVpb6+vtpjVJUgBgBAh6qvry/+SXdn8NC8qXmmS5c0NjZWexSq4LUXns8jSbbcYov0tQ8Uqc+UZ7PeOl3T2LhVtUeBlcKH6gMAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAilJTqVQq1R4COoPH5ryQF57/e/bYa0C1RwFYJg8+U8nGvZMNetVUexRgDfDSvLmZ3/JwBr/7g9UehSpY8sor+euUe9Jrx53SvW7Dao9DFdzz+D/Ss3tNdh+8drVHgZVCEAMAAACgKN4yCQAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYAAAAAEURxAAAAAAoiiAGAAAAQFEEMQAAAACKIogBAAAAUBRBDAAAAICiCGIAAAAAFEUQAwAAAKAoghgAAAAARRHEAAAAACiKIAYAAABAUQQxAAAAAIoiiAEAAABQFEEMAAAAgKIIYgAAAAAURRADAAAAoCiCGAAAAABFEcQAAAAAKIogBgAAAEBRBDEAAAAAiiKIAQAAAFAUQQwAAACAoghiAAAAABRFEAMAAACgKIIYA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-<pre> sample -&gt; 30, 
- Why it does seem easy confessed Farley . It sounds foolish now grinned Darrin . I 'm beginning to feel ashamed of myself .
-Skipping line 31 due to an error: Bobcat failed to parse "I ' m beginning to feel ashamed of myself .".
- sample -&gt; 31, 
- Dave asked with his charming smile . Suppose we talk about yourselves . My but you girls are good to look at .
-</pre>
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-</div>
-</div>
-<div class="jp-OutputArea-child">
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre> sample -&gt; 32, 
- Are you in any trouble here . No I 'm thankful to say . Belle I 'd rather not answer that .
-Skipping line 33 due to an error: Bobcat failed to parse "No I ' m thankful to say .".
- sample -&gt; 33, 
- You 'll present me wo n't you . Assuredly as soon as I come back . I have a little commission to attend to .
-</pre>
-</div>
-</div>
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"/>
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-<pre> sample -&gt; 34, 
- I I really do n't want to dance with him . Dave could only bow which he did . Then other midshipmen were presented .
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-<pre> sample -&gt; 35, 
- Mr Darrin I do n't choose to believe you sir . Dave 's face went crimson then pale . Do you realize what you 're saying Mr Treadwell .
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-<pre> sample -&gt; 36, 
- The orders were ringing out sharply now . The midshipmen were being marched in to dinner . Hardly a word did Dalzell speak as he ate .
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"/>
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-<pre> sample -&gt; 37, 
- To night I hope replied Darrin . Oh then I must get busy . Why I 'm to represent you Darry .
-</pre>
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-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">KeyboardInterrupt</span>                         Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[2], line 9</span>
-<span class="ansi-green-intense-fg ansi-bold">      7</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
-<span class="ansi-green-intense-fg ansi-bold">      8</span>     <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)"> sample -&gt; </span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>i<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">, </span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span style="color: rgb(175,0,0)"> </span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>line<span style="color: rgb(98,98,98)">.</span>strip()<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">'</span>)
-<span class="ansi-green-fg">----&gt; 9</span>     diagram <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">reader</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">text2circuit</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">line</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">strip</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     10</span>     diagram<span style="color: rgb(98,98,98)">.</span>draw(figsize<span style="color: rgb(98,98,98)">=</span>(<span style="color: rgb(98,98,98)">12</span>, <span style="color: rgb(98,98,98)">8</span>), foliated<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>, draw_type_labels<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>, color_boxes<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>, color_wire<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>)
-<span class="ansi-green-intense-fg ansi-bold">     11</span> <span class="ansi-bold" style="color: rgb(0,135,0)">except</span> <span class="ansi-bold" style="color: rgb(215,95,95)">Exception</span> <span class="ansi-bold" style="color: rgb(0,135,0)">as</span> e:
-<span class="ansi-green-intense-fg ansi-bold">     12</span>     <span style="color: rgb(95,135,135)"># Skip lines that cause exceptions and print an error message</span>
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/lambeq-discocirc/lambeq_discocirc/reader.py:297</span>, in <span class="ansi-cyan-fg">DisCoCircReader.text2circuit</span><span class="ansi-blue-fg">(self, text, sandwich, break_cycles, pruned_nouns, rewrite_rules, foliated_frame_labels)</span>
-<span class="ansi-green-intense-fg ansi-bold">    294</span> noun_boxes <span style="color: rgb(98,98,98)">=</span> []
-<span class="ansi-green-intense-fg ansi-bold">    296</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> i, sentence <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span style="color: rgb(0,135,0)">enumerate</span>(sentences):
-<span class="ansi-green-fg">--&gt; 297</span>     tree <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_sentence2tree</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentence</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">break_cycles</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    299</span>     tree_toks <span style="color: rgb(98,98,98)">=</span> tree<span style="color: rgb(98,98,98)">.</span>get_words()
-<span class="ansi-green-intense-fg ansi-bold">    300</span>     reidxr <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_calculate_reindices(sentence, tree_toks)
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/lambeq-discocirc/lambeq_discocirc/reader.py:68</span>, in <span class="ansi-cyan-fg">DisCoCircReader._sentence2tree</span><span class="ansi-blue-fg">(self, sentence, break_cycles)</span>
-<span class="ansi-green-intense-fg ansi-bold">     64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_sentence2tree</span>(<span style="color: rgb(0,135,0)">self</span>, sentence: <span style="color: rgb(0,135,0)">str</span>, break_cycles: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">True</span>):
-<span class="ansi-green-intense-fg ansi-bold">     65</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Get the pregroup tree from a pregroup diagram generated by</span>
-<span class="ansi-green-intense-fg ansi-bold">     66</span> <span style="color: rgb(175,0,0)">    the CCG parser."""</span>
-<span class="ansi-green-fg">---&gt; 68</span>     d <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">ccg_parser</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">sentence2diagram</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentence</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     70</span>     root <span style="color: rgb(98,98,98)">=</span> d<span style="color: rgb(98,98,98)">.</span>to_pregroup_tree(break_cycles<span style="color: rgb(98,98,98)">=</span>break_cycles)
-<span class="ansi-green-intense-fg ansi-bold">     72</span>     <span style="color: rgb(95,135,135)"># If diagram returns free wires, corrects by adding missing noun</span>
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/text2diagram/ccg_parser.py:232</span>, in <span class="ansi-cyan-fg">CCGParser.sentence2diagram</span><span class="ansi-blue-fg">(self, sentence, tokenised, planar, collapse_noun_phrases, suppress_exceptions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    228</span>         <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">ValueError</span>(<span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">`tokenised` set to `True`, but variable </span><span style="color: rgb(175,0,0)">'</span>
-<span class="ansi-green-intense-fg ansi-bold">    229</span>                          <span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">`sentence` does not have type </span><span style="color: rgb(175,0,0)">'</span>
-<span class="ansi-green-intense-fg ansi-bold">    230</span>                          <span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">`list[str]`.</span><span style="color: rgb(175,0,0)">'</span>)
-<span class="ansi-green-intense-fg ansi-bold">    231</span>     sent: <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">str</span>] <span style="color: rgb(98,98,98)">=</span> [<span style="color: rgb(0,135,0)">str</span>(token) <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> token <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> sentence]
-<span class="ansi-green-fg">--&gt; 232</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">sentences2diagrams</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    233</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg">sent</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    234</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">planar</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">planar</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    235</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">collapse_noun_phrases</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">collapse_noun_phrases</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    236</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    237</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    238</span> <span class="ansi-yellow-bg">                    </span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">VerbosityLevel</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">SUPPRESS</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">value</span><span class="ansi-yellow-bg">)</span>[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-intense-fg ansi-bold">    239</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-intense-fg ansi-bold">    240</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span style="color: rgb(0,135,0)">isinstance</span>(sentence, <span style="color: rgb(0,135,0)">str</span>):
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/text2diagram/ccg_parser.py:163</span>, in <span class="ansi-cyan-fg">CCGParser.sentences2diagrams</span><span class="ansi-blue-fg">(self, sentences, tokenised, planar, collapse_noun_phrases, suppress_exceptions, verbose)</span>
-<span class="ansi-green-intense-fg ansi-bold">    123</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">sentences2diagrams</span>(<span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    124</span>                        sentences: SentenceBatchType,
-<span class="ansi-green-intense-fg ansi-bold">    125</span>                        tokenised: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    128</span>                        suppress_exceptions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    129</span>                        verbose: <span style="color: rgb(0,135,0)">str</span> <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[Diagram <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>]:
-<span class="ansi-green-intense-fg ansi-bold">    130</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Parse multiple sentences into a list of lambeq diagrams.</span>
-<span class="ansi-green-intense-fg ansi-bold">    131</span> 
-<span class="ansi-green-intense-fg ansi-bold">    132</span> <span style="color: rgb(175,0,0)">    Parameters</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    161</span> 
-<span class="ansi-green-intense-fg ansi-bold">    162</span> <span style="color: rgb(175,0,0)">    """</span>
-<span class="ansi-green-fg">--&gt; 163</span>     trees <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">sentences2trees</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentences</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    164</span> <span class="ansi-yellow-bg">                                 </span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">suppress_exceptions</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    165</span> <span class="ansi-yellow-bg">                                 </span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">tokenised</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    166</span> <span class="ansi-yellow-bg">                                 </span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    167</span>     diagrams: <span style="color: rgb(0,135,0)">list</span>[Diagram <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>] <span style="color: rgb(98,98,98)">=</span> []
-<span class="ansi-green-intense-fg ansi-bold">    168</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> verbose <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/text2diagram/bobcat_parser.py:280</span>, in <span class="ansi-cyan-fg">BobcatParser.sentences2trees</span><span class="ansi-blue-fg">(self, sentences, tokenised, suppress_exceptions, verbose)</span>
-<span class="ansi-green-intense-fg ansi-bold">    278</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> verbose <span style="color: rgb(98,98,98)">==</span> VerbosityLevel<span style="color: rgb(98,98,98)">.</span>TEXT<span style="color: rgb(98,98,98)">.</span>value:
-<span class="ansi-green-intense-fg ansi-bold">    279</span>     <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">Tagging sentences.</span><span style="color: rgb(175,0,0)">'</span>, file<span style="color: rgb(98,98,98)">=</span>sys<span style="color: rgb(98,98,98)">.</span>stderr)
-<span class="ansi-green-fg">--&gt; 280</span> tag_results <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">tagger</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">sentences</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">verbose</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    281</span> tags <span style="color: rgb(98,98,98)">=</span> tag_results<span style="color: rgb(98,98,98)">.</span>tags
-<span class="ansi-green-intense-fg ansi-bold">    282</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> verbose <span style="color: rgb(98,98,98)">==</span> VerbosityLevel<span style="color: rgb(98,98,98)">.</span>TEXT<span style="color: rgb(98,98,98)">.</span>value:
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/bobcat/tagger.py:402</span>, in <span class="ansi-cyan-fg">Tagger.__call__</span><span class="ansi-blue-fg">(self, inputs, batch_size, verbose)</span>
-<span class="ansi-green-intense-fg ansi-bold">    394</span> output <span style="color: rgb(98,98,98)">=</span> TaggerOutput(tags<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>model<span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>tags,
-<span class="ansi-green-intense-fg ansi-bold">    395</span>                       cats<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>model<span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>cats,
-<span class="ansi-green-intense-fg ansi-bold">    396</span>                       sentences<span style="color: rgb(98,98,98)">=</span>[])
-<span class="ansi-green-intense-fg ansi-bold">    398</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> i <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> trange(<span style="color: rgb(98,98,98)">0</span>, <span style="color: rgb(0,135,0)">len</span>(inputs), batch_size,
-<span class="ansi-green-intense-fg ansi-bold">    399</span>                 desc<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">'</span><span style="color: rgb(175,0,0)">Tagging sentences</span><span style="color: rgb(175,0,0)">'</span>,
-<span class="ansi-green-intense-fg ansi-bold">    400</span>                 leave<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    401</span>                 disable<span style="color: rgb(98,98,98)">=</span>verbose <span style="color: rgb(98,98,98)">!=</span> VerbosityLevel<span style="color: rgb(98,98,98)">.</span>PROGRESS<span style="color: rgb(98,98,98)">.</span>value):
-<span class="ansi-green-fg">--&gt; 402</span>     output<span style="color: rgb(98,98,98)">.</span>sentences<span style="color: rgb(98,98,98)">.</span>extend(<span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">parse</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">inputs</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg">i</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg">i</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">+</span><span class="ansi-yellow-bg">batch_size</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">)</span>)
-<span class="ansi-green-intense-fg ansi-bold">    404</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> output
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/bobcat/tagger.py:333</span>, in <span class="ansi-cyan-fg">Tagger.parse</span><span class="ansi-blue-fg">(self, inputs)</span>
-<span class="ansi-green-intense-fg ansi-bold">    331</span> <span style="color: rgb(175,0,0)">"""Parse a batch of sentences."""</span>
-<span class="ansi-green-intense-fg ansi-bold">    332</span> encodings <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>prepare_inputs(inputs, word_mask<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>)
-<span class="ansi-green-fg">--&gt; 333</span> outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">model</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">{</span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">torch</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">as_tensor</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">v</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">model</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    334</span> <span class="ansi-yellow-bg">                        </span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">for</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">v</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(175,0,255)">in</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">encodings</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">items</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">}</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    336</span> tag_output: <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">list</span>[TagListT]] <span style="color: rgb(98,98,98)">=</span> []
-<span class="ansi-green-intense-fg ansi-bold">    337</span> span_output: <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">list</span>[TagListT]] <span style="color: rgb(98,98,98)">=</span> []
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
-<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
-<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
-<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
-<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
-<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-fg">~/Documents/Code/Lambeq/Dev/Lambeq/bug-fixes/lambeq/bobcat/tagger.py:132</span>, in <span class="ansi-cyan-fg">BertForChartClassification.forward</span><span class="ansi-blue-fg">(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, tag_labels, span_labels, word_mask, output_attentions, output_hidden_states, return_dict)</span>
-<span class="ansi-green-intense-fg ansi-bold">    114</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">forward</span>(
-<span class="ansi-green-intense-fg ansi-bold">    115</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    116</span>     input_ids: torch<span style="color: rgb(98,98,98)">.</span>LongTensor <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    127</span>     return_dict: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-<span class="ansi-green-intense-fg ansi-bold">    128</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ChartClassifierOutput <span style="color: rgb(98,98,98)">|</span> <span style="color: rgb(0,135,0)">tuple</span>[Any, <span style="color: rgb(98,98,98)">.</span><span style="color: rgb(98,98,98)">.</span><span style="color: rgb(98,98,98)">.</span>]:
-<span class="ansi-green-intense-fg ansi-bold">    129</span>     return_dict <span style="color: rgb(98,98,98)">=</span> (return_dict <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> return_dict <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-<span class="ansi-green-intense-fg ansi-bold">    130</span>                    <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>use_return_dict)
-<span class="ansi-green-fg">--&gt; 132</span>     outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">bert</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    133</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">input_ids</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    134</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    135</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">token_type_ids</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">token_type_ids</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    136</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">position_ids</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">position_ids</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    137</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    138</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">inputs_embeds</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">inputs_embeds</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    139</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    140</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    141</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    142</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    144</span>     sequence_output <span style="color: rgb(98,98,98)">=</span> outputs[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-intense-fg ansi-bold">    145</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> word_mask <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-<span class="ansi-green-intense-fg ansi-bold">    146</span>         <span style="color: rgb(95,135,135)"># remove ignored tensors and pack remaining ones</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
-<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
-<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
-<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
-<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
-<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:1021</span>, in <span class="ansi-cyan-fg">BertModel.forward</span><span class="ansi-blue-fg">(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1012</span> head_mask <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>get_head_mask(head_mask, <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>config<span style="color: rgb(98,98,98)">.</span>num_hidden_layers)
-<span class="ansi-green-intense-fg ansi-bold">   1014</span> embedding_output <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>embeddings(
-<span class="ansi-green-intense-fg ansi-bold">   1015</span>     input_ids<span style="color: rgb(98,98,98)">=</span>input_ids,
-<span class="ansi-green-intense-fg ansi-bold">   1016</span>     position_ids<span style="color: rgb(98,98,98)">=</span>position_ids,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1019</span>     past_key_values_length<span style="color: rgb(98,98,98)">=</span>past_key_values_length,
-<span class="ansi-green-intense-fg ansi-bold">   1020</span> )
-<span class="ansi-green-fg">-&gt; 1021</span> encoder_outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">encoder</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">   1022</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">embedding_output</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1023</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">extended_attention_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1024</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">head_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1025</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoder_hidden_states</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">encoder_hidden_states</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1026</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoder_attention_mask</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">encoder_extended_attention_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1027</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">past_key_values</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">past_key_values</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1028</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">use_cache</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">use_cache</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1029</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1030</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">output_hidden_states</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1031</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">return_dict</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   1032</span> <span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1033</span> sequence_output <span style="color: rgb(98,98,98)">=</span> encoder_outputs[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-intense-fg ansi-bold">   1034</span> pooled_output <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>pooler(sequence_output) <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>pooler <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
-<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
-<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
-<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
-<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
-<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:610</span>, in <span class="ansi-cyan-fg">BertEncoder.forward</span><span class="ansi-blue-fg">(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)</span>
-<span class="ansi-green-intense-fg ansi-bold">    601</span>     layer_outputs <span style="color: rgb(98,98,98)">=</span> torch<span style="color: rgb(98,98,98)">.</span>utils<span style="color: rgb(98,98,98)">.</span>checkpoint<span style="color: rgb(98,98,98)">.</span>checkpoint(
-<span class="ansi-green-intense-fg ansi-bold">    602</span>         create_custom_forward(layer_module),
-<span class="ansi-green-intense-fg ansi-bold">    603</span>         hidden_states,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    607</span>         encoder_attention_mask,
-<span class="ansi-green-intense-fg ansi-bold">    608</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    609</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">--&gt; 610</span>     layer_outputs <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">layer_module</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    611</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">hidden_states</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    612</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">attention_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    613</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">layer_head_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    614</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">encoder_hidden_states</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    615</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">encoder_attention_mask</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    616</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">past_key_value</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    617</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">output_attentions</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    618</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    620</span> hidden_states <span style="color: rgb(98,98,98)">=</span> layer_outputs[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-intense-fg ansi-bold">    621</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> use_cache:
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
-<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
-<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
-<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
-<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
-<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:538</span>, in <span class="ansi-cyan-fg">BertLayer.forward</span><span class="ansi-blue-fg">(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    535</span>     cross_attn_present_key_value <span style="color: rgb(98,98,98)">=</span> cross_attention_outputs[<span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">1</span>]
-<span class="ansi-green-intense-fg ansi-bold">    536</span>     present_key_value <span style="color: rgb(98,98,98)">=</span> present_key_value <span style="color: rgb(98,98,98)">+</span> cross_attn_present_key_value
-<span class="ansi-green-fg">--&gt; 538</span> layer_output <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">apply_chunking_to_forward</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    539</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">feed_forward_chunk</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">chunk_size_feed_forward</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">seq_len_dim</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">attention_output</span>
-<span class="ansi-green-intense-fg ansi-bold">    540</span> <span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    541</span> outputs <span style="color: rgb(98,98,98)">=</span> (layer_output,) <span style="color: rgb(98,98,98)">+</span> outputs
-<span class="ansi-green-intense-fg ansi-bold">    543</span> <span style="color: rgb(95,135,135)"># if decoder, return the attn key/values as the last output</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/pytorch_utils.py:246</span>, in <span class="ansi-cyan-fg">apply_chunking_to_forward</span><span class="ansi-blue-fg">(forward_fn, chunk_size, chunk_dim, *input_tensors)</span>
-<span class="ansi-green-intense-fg ansi-bold">    243</span>     <span style="color: rgb(95,135,135)"># concatenate output at same dimension</span>
-<span class="ansi-green-intense-fg ansi-bold">    244</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> torch<span style="color: rgb(98,98,98)">.</span>cat(output_chunks, dim<span style="color: rgb(98,98,98)">=</span>chunk_dim)
-<span class="ansi-green-fg">--&gt; 246</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_fn</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">input_tensors</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:550</span>, in <span class="ansi-cyan-fg">BertLayer.feed_forward_chunk</span><span class="ansi-blue-fg">(self, attention_output)</span>
-<span class="ansi-green-intense-fg ansi-bold">    549</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">feed_forward_chunk</span>(<span style="color: rgb(0,135,0)">self</span>, attention_output):
-<span class="ansi-green-fg">--&gt; 550</span>     intermediate_output <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">intermediate</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">attention_output</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    551</span>     layer_output <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>output(intermediate_output, attention_output)
-<span class="ansi-green-intense-fg ansi-bold">    552</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> layer_output
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
-<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
-<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
-<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
-<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
-<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:450</span>, in <span class="ansi-cyan-fg">BertIntermediate.forward</span><span class="ansi-blue-fg">(self, hidden_states)</span>
-<span class="ansi-green-intense-fg ansi-bold">    449</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">forward</span>(<span style="color: rgb(0,135,0)">self</span>, hidden_states: torch<span style="color: rgb(98,98,98)">.</span>Tensor) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> torch<span style="color: rgb(98,98,98)">.</span>Tensor:
-<span class="ansi-green-fg">--&gt; 450</span>     hidden_states <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dense</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">hidden_states</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    451</span>     hidden_states <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>intermediate_act_fn(hidden_states)
-<span class="ansi-green-intense-fg ansi-bold">    452</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> hidden_states
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1511</span>, in <span class="ansi-cyan-fg">Module._wrapped_call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1509</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_compiled_call_impl(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)  <span style="color: rgb(95,135,135)"># type: ignore[misc]</span>
-<span class="ansi-green-intense-fg ansi-bold">   1510</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">-&gt; 1511</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_call_impl</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1520</span>, in <span class="ansi-cyan-fg">Module._call_impl</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1515</span> <span style="color: rgb(95,135,135)"># If we don't have any hooks, we want to skip the rest of the logic in</span>
-<span class="ansi-green-intense-fg ansi-bold">   1516</span> <span style="color: rgb(95,135,135)"># this function, and just call forward.</span>
-<span class="ansi-green-intense-fg ansi-bold">   1517</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_forward_pre_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1518</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_pre_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_backward_hooks
-<span class="ansi-green-intense-fg ansi-bold">   1519</span>         <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_hooks <span class="ansi-bold" style="color: rgb(175,0,255)">or</span> _global_forward_pre_hooks):
-<span class="ansi-green-fg">-&gt; 1520</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">forward_call</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1522</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
-<span class="ansi-green-intense-fg ansi-bold">   1523</span>     result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-fg">~/.spyenv/lib/python3.11/site-packages/torch/nn/modules/linear.py:116</span>, in <span class="ansi-cyan-fg">Linear.forward</span><span class="ansi-blue-fg">(self, input)</span>
-<span class="ansi-green-intense-fg ansi-bold">    115</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">forward</span>(<span style="color: rgb(0,135,0)">self</span>, <span style="color: rgb(0,135,0)">input</span>: Tensor) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> Tensor:
-<span class="ansi-green-fg">--&gt; 116</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">F</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">linear</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">input</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">weight</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">bias</span><span class="ansi-yellow-bg">)</span>
-
-<span class="ansi-red-fg">KeyboardInterrupt</span>: </pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</main>
-</body>
-</html>

From 353df2f6117c0073eb04d64ae40084f3d96bee38 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Tue, 19 Nov 2024 11:55:35 +0000
Subject: [PATCH 26/41] cosmetic readability fixes

---
 lambeq/backend/drawing/drawing_backend.py |  9 +++++----
 lambeq/backend/drawing/tikz_backend.py    | 16 ++++++++--------
 2 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index 8c9493bb..2778d3f3 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -30,7 +30,7 @@
 DEFAULT_MARGINS = (.05, .1)
 DEFAULT_ASPECT = 'equal'
 
-WIRE_COLORS = [
+WIRE_COLORS: list[str] = [
     '#9c540e', '#f4a940', '#066ee2', '#d03b2d', '#7fd68b',
     '#574cfa', '#49a141', '#a629b3', '#271296', '#ff6347',
     '#adff2f', '#7446f2', '#007765', '#b60539', '#ff00ff',
@@ -196,7 +196,7 @@ def draw_spiders(self, drawable: DrawableDiagram, **params) -> None:
 
     def _get_wire_color(self, wire_id : int, **params) -> str:
         """
-         Retrieves a color that uniquely represent a given wire ID.
+        Retrieves a color that uniquely represent a given wire ID.
 
         Parameters
         ----------
@@ -206,9 +206,10 @@ def _get_wire_color(self, wire_id : int, **params) -> str:
         **params:
             Additional parameters.
 
-        Returns:
+        Returns
+        -------
         wire_color : str
-            The Hex color of the wire, represented as a string.
+            The hex color of the wire, represented as a string.
 
         """
         if not params.get('color_wires') or wire_id == 0:
diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index a25cb2cb..718d9083 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -43,16 +43,16 @@ def __init__(self, use_tikzstyles: bool = False):
 
     def format_color(self, color: str) -> str:
         """
-         Returns RGB value for the Hex.
+        Returns RGB value for the hex.
 
         Parameters
         ----------
         color : str
-            The Hex color code, represented as a string.
+            The hex color code, represented as a string.
 
         Returns
-        ----------
-        rgb-color: str
+        -------
+        rgb-color : str
             The RGB color format, represented as a string.
 
         """
@@ -61,16 +61,16 @@ def format_color(self, color: str) -> str:
 
     def get_rgb_for_hex(self, hexcode: str) -> str:
         """
-         Helper method to convert Hex to RGB
+        Helper method to convert hex to RGB
 
         Parameters
         ----------
         hexcode : str
-            The Hex color, represented as a string.
+            The hex color, represented as a string.
 
         Returns
-        ----------
-        rgb-color: str
+        -------
+        rgb-color : str
             The RGB color, represented as a string.
 
         """

From 8d43b7912500948abb9b02c0822cd919dfb9f369 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Tue, 19 Nov 2024 16:51:45 +0000
Subject: [PATCH 27/41] refactor wire colour formatting like boxes

---
 lambeq/backend/drawing/drawing_backend.py |  6 ++++++
 lambeq/backend/drawing/tikz_backend.py    | 20 +++++++++-----------
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/lambeq/backend/drawing/drawing_backend.py b/lambeq/backend/drawing/drawing_backend.py
index 2778d3f3..867ca73f 100644
--- a/lambeq/backend/drawing/drawing_backend.py
+++ b/lambeq/backend/drawing/drawing_backend.py
@@ -38,6 +38,12 @@
     '#740127', '#e2074c', '#0252a1', '#fea431', '#205356',
     '#450d06', '#d17800', '#3831a0', '#ff4500', '#d8bfd8'
 ]
+WIRE_COLORS_NAMES: dict[str, str] = {
+    '#ffffff' : '#ffffff',
+    '#000000' : '#000000'
+}
+for color in WIRE_COLORS:
+    WIRE_COLORS_NAMES[color] = color
 
 FRAME_COLORS: list[str] = [
     '#fbe8e7', '#fee1ba', '#fff9e5', '#e8f8ea', '#dcfbf5',
diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 718d9083..ebd5e70d 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -45,17 +45,8 @@ def format_color(self, color: str) -> str:
         """
         Returns RGB value for the hex.
 
-        Parameters
-        ----------
-        color : str
-            The hex color code, represented as a string.
-
-        Returns
-        -------
-        rgb-color : str
-            The RGB color format, represented as a string.
-
-        """
+    @staticmethod
+    def format_color(color: str) -> str:
         hexcode = COLORS[color]
         return self.get_rgb_for_hex(hexcode=hexcode)
 
@@ -78,6 +69,13 @@ def get_rgb_for_hex(self, hexcode: str) -> str:
             int(hex, 16) for hex in [hexcode[1:3], hexcode[3:5], hexcode[5:]]]
         return f'{{rgb,255: red,{rgb[0]}; green,{rgb[1]}; blue,{rgb[2]}}}'
 
+    @staticmethod
+    def format_wire_color(color: str) -> str:
+        hexcode = WIRE_COLORS_NAMES[color]
+        rgb = [
+            int(hex, 16) for hex in [hexcode[1:3], hexcode[3:5], hexcode[5:]]]
+        return f'{{rgb,255: red,{rgb[0]}; green,{rgb[1]}; blue,{rgb[2]}}}'
+
     def add_node(self,
                  x: float,
                  y: float,

From 78fbfee7c5970111e757cd345554ddb46a97da75 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Tue, 19 Nov 2024 16:53:52 +0000
Subject: [PATCH 28/41] wires width changes for Tikz backend

---
 lambeq/backend/drawing/drawing.py      | 12 +++++--
 lambeq/backend/drawing/tikz_backend.py | 44 +++++++++-----------------
 2 files changed, 24 insertions(+), 32 deletions(-)

diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index 0c9ea014..f113a70b 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -48,6 +48,8 @@
 from lambeq.backend.drawing.tikz_backend import TikzBackend
 from lambeq.backend.grammar import Box, Diagram
 
+WIRES_DEFAULT_WIDTH_MAT = 1.25
+WIRES_DEFAULT_WIDTH_TIKZ = 1.25 * 0.4  # Tikz default standard is 0.4pt
 
 if TYPE_CHECKING:
     from IPython.core.display import HTML as HTML_ty
@@ -126,10 +128,14 @@ def draw(diagram: Diagram, **params) -> None:
         backend: DrawingBackend = params.pop('backend')
     elif params.get('to_tikz', False):
         backend = TikzBackend(
-            use_tikzstyles=params.get('use_tikzstyles', None))
+            use_tikzstyles=params.get('use_tikzstyles', None),
+            wires_linewidth=params.get('wires_linewidth',
+                                       WIRES_DEFAULT_WIDTH_TIKZ))
     else:
-        backend = MatBackend(figsize=params.get('figsize', None),
-                             wires_linewidth=params.get('wires_width', 1.25))
+        backend = (
+            MatBackend(figsize=params.get('figsize', None),
+                       wires_linewidth=params.get('wires_linewidth',
+                                                  WIRES_DEFAULT_WIDTH_MAT)))
 
     min_size = 0.01
     max_v = max([v for point in ([point.coordinates for point in
diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index ebd5e70d..f4bb335e 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -23,7 +23,8 @@
 from math import sqrt
 
 from lambeq.backend.drawing.drawable import DrawableDiagram
-from lambeq.backend.drawing.drawing_backend import COLORS, DrawingBackend
+from lambeq.backend.drawing.drawing_backend import (COLORS, WIRE_COLORS_NAMES,
+                                                    DrawingBackend)
 from lambeq.backend.drawing.helpers import drawn_as_spider
 from lambeq.backend.grammar import Spider
 
@@ -31,7 +32,8 @@
 class TikzBackend(DrawingBackend):
     """ Tikz drawing backend. """
 
-    def __init__(self, use_tikzstyles: bool = False):
+    def __init__(self, use_tikzstyles: bool = False,
+                 wires_linewidth: float = 0.4):
         self.use_tikzstyles = use_tikzstyles
         self.node_styles: list[str] = []
         self.edge_styles: list[str] = []
@@ -40,31 +42,11 @@ def __init__(self, use_tikzstyles: bool = False):
         self.edgelayer: list[str] = []
         self.label_layer: list[str] = []
         self.max_width: float = 0
-
-    def format_color(self, color: str) -> str:
-        """
-        Returns RGB value for the hex.
+        self.wires_linewidth = wires_linewidth
 
     @staticmethod
     def format_color(color: str) -> str:
         hexcode = COLORS[color]
-        return self.get_rgb_for_hex(hexcode=hexcode)
-
-    def get_rgb_for_hex(self, hexcode: str) -> str:
-        """
-        Helper method to convert hex to RGB
-
-        Parameters
-        ----------
-        hexcode : str
-            The hex color, represented as a string.
-
-        Returns
-        -------
-        rgb-color : str
-            The RGB color, represented as a string.
-
-        """
         rgb = [
             int(hex, 16) for hex in [hexcode[1:3], hexcode[3:5], hexcode[5:]]]
         return f'{{rgb,255: red,{rgb[0]}; green,{rgb[1]}; blue,{rgb[2]}}}'
@@ -186,18 +168,22 @@ def draw_wire(self,
             wire_style_name = (f'{color_name}_wire'
                                if color != '#000000' else 'black_wire')
             wire_style = (f'\\tikzstyle{{{wire_style_name}}}='
-                          f'[-, draw={self.get_rgb_for_hex(color)}]\n')
+                          f'[-, draw={self.format_wire_color(color)}')
+
+            # Concatenate additional styles like looseness if present
+            if style:
+                wire_style += f', {style}'
+            wire_style += f', line width={self.wires_linewidth}]\n'
+
             if wire_style not in self.edge_styles:
                 self.edge_styles.append(wire_style)
             wire_options = f'style={wire_style_name}'
 
-            # Concatenate additional styles like looseness if present
+        else:
+            wire_options = f'-, draw={self.format_wire_color(color)}'
+            wire_options += f', line width={self.wires_linewidth}'
             if style:
                 wire_options += f', {style}'
-
-        else:
-            wire_options = f'-, draw={self.get_rgb_for_hex(color)}'
-
         cmd = (
             '\\draw [in={}, out={}{}] '
             '({}.center) to ({}.center);\n')

From ba8dc7f7e78062d6552485181d91c8190c6a7da0 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Tue, 19 Nov 2024 17:01:09 +0000
Subject: [PATCH 29/41] fix for flake8 import order error

---
 lambeq/backend/drawing/tikz_backend.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index f4bb335e..698c137e 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -23,8 +23,9 @@
 from math import sqrt
 
 from lambeq.backend.drawing.drawable import DrawableDiagram
-from lambeq.backend.drawing.drawing_backend import (COLORS, WIRE_COLORS_NAMES,
-                                                    DrawingBackend)
+from lambeq.backend.drawing.drawing_backend import (COLORS, DrawingBackend,
+                                                    WIRE_COLORS_NAMES
+                                                    )
 from lambeq.backend.drawing.helpers import drawn_as_spider
 from lambeq.backend.grammar import Spider
 

From ae698575b2d5a7aeac4fced10646ff443f19ffb7 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Wed, 20 Nov 2024 11:59:41 +0000
Subject: [PATCH 30/41] update testcases for frame drawing with wire colouring

---
 tests/backend/test_frame_drawing.py | 346 +++++++++++++++-------------
 1 file changed, 180 insertions(+), 166 deletions(-)

diff --git a/tests/backend/test_frame_drawing.py b/tests/backend/test_frame_drawing.py
index 128bdb61..bb319f67 100644
--- a/tests/backend/test_frame_drawing.py
+++ b/tests/backend/test_frame_drawing.py
@@ -166,39 +166,38 @@
 
 
 tikz_outputs = [
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (2.25, 11.625) {};
 \\node [] (2) at (3.25, 11.625) {};
 \\node [] (3) at (3.25, 12.125) {};
 \\node [] (4) at (2.25, 12.125) {};
-\\node [style=none, fill=white] (5) at (2.75, 11.875) {Alice};
 \\node [] (6) at (4.75, 11.625) {};
 \\node [] (7) at (5.75, 11.625) {};
 \\node [] (8) at (5.75, 12.125) {};
 \\node [] (9) at (4.75, 12.125) {};
-\\node [style=none, fill=white] (10) at (5.25, 11.875) {Bob};
 \\node [] (11) at (7.25, 11.625) {};
 \\node [] (12) at (8.25, 11.625) {};
 \\node [] (13) at (8.25, 12.125) {};
 \\node [] (14) at (7.25, 12.125) {};
-\\node [style=none, fill=white] (15) at (7.75, 11.875) {cake};
 \\node [] (16) at (9.75, 11.625) {};
 \\node [] (17) at (10.75, 11.625) {};
 \\node [] (18) at (10.75, 12.125) {};
 \\node [] (19) at (9.75, 12.125) {};
-\\node [style=none, fill=white] (20) at (10.25, 11.875) {coffee};
 \\node [] (21) at (2.25, 9.125) {};
 \\node [] (22) at (5.75, 9.125) {};
 \\node [] (23) at (5.75, 9.625) {};
 \\node [] (24) at (2.25, 9.625) {};
-\\node [style=none, fill=white] (25) at (4.0, 9.375) {told};
 \\node [] (26) at (-1.5, -1.875) {};
 \\node [] (27) at (14.5, -1.875) {};
 \\node [] (28) at (14.5, 7.125) {};
 \\node [] (29) at (-1.5, 7.125) {};
-\\node [style=none, fill=white] (30) at (6.5, 6.625) {and};
 \\node [] (31) at (-1.0, 1.625) {};
 \\node [] (32) at (3.5, 1.625) {};
 \\node [] (33) at (3.5, 3.625) {};
@@ -207,7 +206,6 @@
 \\node [] (36) at (3.0, 2.375) {};
 \\node [] (37) at (3.0, 2.875) {};
 \\node [] (38) at (-0.5, 2.875) {};
-\\node [style=none, fill=white] (39) at (1.25, 2.625) {eats};
 \\node [] (40) at (4.25, 1.625) {};
 \\node [] (41) at (8.75, 1.625) {};
 \\node [] (42) at (8.75, 3.625) {};
@@ -216,7 +214,6 @@
 \\node [] (45) at (8.25, 2.375) {};
 \\node [] (46) at (8.25, 2.875) {};
 \\node [] (47) at (4.75, 2.875) {};
-\\node [style=none, fill=white] (48) at (6.5, 2.625) {drinks};
 \\node [] (49) at (9.5, -0.875) {};
 \\node [] (50) at (14.0, -0.875) {};
 \\node [] (51) at (14.0, 6.125) {};
@@ -225,151 +222,161 @@
 \\node [] (54) at (12.25, 4.875) {};
 \\node [] (55) at (12.25, 5.375) {};
 \\node [] (56) at (11.25, 5.375) {};
-\\node [style=none, fill=white] (57) at (11.75, 5.125) {runs};
 \\node [] (58) at (10.0, 2.375) {};
 \\node [] (59) at (13.5, 2.375) {};
 \\node [] (60) at (13.5, 2.875) {};
 \\node [] (61) at (10.0, 2.875) {};
-\\node [style=none, fill=white] (62) at (11.75, 2.625) {x};
 \\node [] (63) at (10.0, -0.125) {};
 \\node [] (64) at (11.0, -0.125) {};
 \\node [] (65) at (11.0, 0.375) {};
 \\node [] (66) at (10.0, 0.375) {};
-\\node [style=none, fill=white] (67) at (10.5, 0.125) {y};
 \\node [] (68) at (6.0, -4.375) {};
 \\node [] (69) at (9.5, -4.375) {};
 \\node [] (70) at (9.5, -3.875) {};
 \\node [] (71) at (6.0, -3.875) {};
-\\node [style=none, fill=white] (72) at (7.75, -4.125) {test};
 \\node [] (73) at (3.5, -4.375) {};
 \\node [] (74) at (4.5, -4.375) {};
 \\node [] (75) at (4.5, -3.875) {};
 \\node [] (76) at (3.5, -3.875) {};
-\\node [style=none, fill=white] (77) at (4.0, -4.125) {a};
 \\node [] (78) at (8.5, -6.875) {};
 \\node [] (79) at (9.5, -6.875) {};
 \\node [] (80) at (9.5, -6.375) {};
 \\node [] (81) at (8.5, -6.375) {};
-\\node [style=none, fill=white] (82) at (9.0, -6.625) {b};
 \\node [] (83) at (2.75, 11.625) {};
 \\node [] (84) at (2.75, 9.625) {};
-\\node [style=none, fill=white, right] (85) at (2.85, 11.525) {n};
 \\node [] (86) at (5.25, 11.625) {};
 \\node [] (87) at (5.25, 9.625) {};
-\\node [style=none, fill=white, right] (88) at (5.35, 11.525) {n};
 \\node [] (89) at (2.75, 9.125) {};
 \\node [] (90) at (2.75, 7.125) {};
-\\node [style=none, fill=white, right] (91) at (2.85, 9.025) {n};
 \\node [] (92) at (5.25, 9.125) {};
 \\node [] (93) at (5.25, 7.125) {};
-\\node [style=none, fill=white, right] (94) at (5.35, 9.025) {n};
 \\node [] (95) at (7.75, 11.625) {};
 \\node [] (96) at (7.75, 7.125) {};
-\\node [style=none, fill=white, right] (97) at (7.85, 11.525) {n};
 \\node [] (98) at (10.25, 11.625) {};
 \\node [] (99) at (10.25, 7.125) {};
-\\node [style=none, fill=white, right] (100) at (10.35, 11.525) {n};
 \\node [] (101) at (0.0, 3.625) {};
 \\node [] (102) at (0.0, 2.875) {};
-\\node [style=none, fill=white, right] (103) at (0.1, 3.625) {n};
 \\node [] (104) at (2.5, 3.625) {};
 \\node [] (105) at (2.5, 2.875) {};
-\\node [style=none, fill=white, right] (106) at (2.6, 3.625) {n};
 \\node [] (107) at (0.0, 2.375) {};
 \\node [] (108) at (0.0, 1.625) {};
-\\node [style=none, fill=white, right] (109) at (0.1, 2.275) {n};
 \\node [] (110) at (2.5, 2.375) {};
 \\node [] (111) at (2.5, 1.625) {};
-\\node [style=none, fill=white, right] (112) at (2.6, 2.275) {n};
 \\node [] (113) at (5.25, 3.625) {};
 \\node [] (114) at (5.25, 2.875) {};
-\\node [style=none, fill=white, right] (115) at (5.35, 3.625) {n};
 \\node [] (116) at (7.75, 3.625) {};
 \\node [] (117) at (7.75, 2.875) {};
-\\node [style=none, fill=white, right] (118) at (7.85, 3.625) {n};
 \\node [] (119) at (5.25, 2.375) {};
 \\node [] (120) at (5.25, 1.625) {};
-\\node [style=none, fill=white, right] (121) at (5.35, 2.275) {n};
 \\node [] (122) at (7.75, 2.375) {};
 \\node [] (123) at (7.75, 1.625) {};
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 \\node [] (120) at (5.25, 1.625) {};
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 \\node [] (122) at (7.75, 2.375) {};
 \\node [] (123) at (7.75, 1.625) {};
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 \\node [] (125) at (11.75, 6.125) {};
 \\node [] (126) at (11.75, 5.375) {};
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 \\node [] (128) at (11.75, 4.875) {};
 \\node [] (129) at (11.75, 2.875) {};
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 \\node [] (135) at (10.5, -0.875) {};
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 \\node [] (137) at (13.0, 2.375) {};
 \\node [] (138) at (13.0, -0.875) {};
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 \\node [] (141) at (6.5, -3.875) {};
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 \\node [] (144) at (9.0, -3.875) {};
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+\\draw [-, fill={rgb,255: red,224; green,224; blue,224}] (68.center) to (69.center) to (70.center) to (71.center) to (68.center);
+\\draw [-, fill={rgb,255: red,224; green,224; blue,224}] (73.center) to (74.center) to (75.center) to (76.center) to (73.center);
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+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (122.center) to (123.center);
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+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (128.center) to (129.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (131.center) to (132.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (134.center) to (135.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (137.center) to (138.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,244; green,169; blue,64}, line width=0.5] (140.center) to (141.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,6; green,110; blue,226}, line width=0.5] (143.center) to (144.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,156; green,84; blue,14}, line width=0.5] (146.center) to (147.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,6; green,110; blue,226}, line width=0.5] (149.center) to (150.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,156; green,84; blue,14}, line width=0.5] (152.center) to (153.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,244; green,169; blue,64}, line width=0.5] (155.center) to (156.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (2.75, 11.875) {Alice};
+\\node [style=none] (10) at (5.25, 11.875) {Bob};
+\\node [style=none] (15) at (7.75, 11.875) {cake};
+\\node [style=none] (20) at (10.25, 11.875) {coffee};
+\\node [style=none] (25) at (4.0, 9.375) {told};
+\\node [style=none] (30) at (6.5, 6.625) {and};
+\\node [style=none] (39) at (1.25, 2.625) {eats};
+\\node [style=none] (48) at (6.5, 2.625) {drinks};
+\\node [style=none] (57) at (11.75, 5.125) {runs};
+\\node [style=none] (62) at (11.75, 2.625) {x};
+\\node [style=none] (67) at (10.5, 0.125) {y};
+\\node [style=none] (72) at (7.75, -4.125) {test};
+\\node [style=none] (77) at (4.0, -4.125) {a};
+\\node [style=none] (82) at (9.0, -6.625) {b};
+\\node [style=none, right] (85) at (2.85, 11.525) {n};
+\\node [style=none, right] (88) at (5.35, 11.525) {n};
+\\node [style=none, right] (91) at (2.85, 9.025) {n};
+\\node [style=none, right] (94) at (5.35, 9.025) {n};
+\\node [style=none, right] (97) at (7.85, 11.525) {n};
+\\node [style=none, right] (100) at (10.35, 11.525) {n};
+\\node [style=none, right] (103) at (0.1, 3.625) {n};
+\\node [style=none, right] (106) at (2.6, 3.625) {n};
+\\node [style=none, right] (109) at (0.1, 2.275) {n};
+\\node [style=none, right] (112) at (2.6, 2.275) {n};
+\\node [style=none, right] (115) at (5.35, 3.625) {n};
+\\node [style=none, right] (118) at (7.85, 3.625) {n};
+\\node [style=none, right] (121) at (5.35, 2.275) {n};
+\\node [style=none, right] (124) at (7.85, 2.275) {n};
+\\node [style=none, right] (127) at (11.85, 6.125) {n};
+\\node [style=none, right] (130) at (11.85, 4.775) {n};
+\\node [style=none, right] (133) at (10.6, 2.275) {s};
+\\node [style=none, right] (136) at (10.6, -0.225) {s};
+\\node [style=none, right] (139) at (13.1, 2.275) {s};
+\\node [style=none, right] (142) at (6.6, -1.975) {n};
+\\node [style=none, right] (145) at (9.1, -1.975) {n};
+\\node [style=none, right] (148) at (4.1, -1.975) {n};
+\\node [style=none, right] (151) at (9.1, -4.475) {n};
+\\node [style=none, right] (154) at (4.1, -4.475) {n};
+\\node [style=none, right] (157) at (6.6, -4.475) {n};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
@@ -605,7 +619,7 @@ def test_drawable_generation(diagram, drawable):
 @pytest.mark.parametrize('diagram, tikz', zip(diagrams, tikz_outputs))
 def test_tikz_drawing(diagram, tikz, capsys):
 
-    diagram.draw(backend=TikzBackend(), color_boxes=False)
+    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=False, color_boxes=False)
     tikz_op, _ = capsys.readouterr()
 
     assert tikz_op == tikz
@@ -614,7 +628,7 @@ def test_tikz_drawing(diagram, tikz, capsys):
 @pytest.mark.parametrize('diagram, tikz', zip(diagrams, colored_tikz_outputs))
 def test_tikz_colored_drawing(diagram, tikz, capsys):
 
-    diagram.draw(backend=TikzBackend(), color_boxes=True)
+    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=True, color_boxes=True)
     tikz_op, _ = capsys.readouterr()
 
     assert tikz_op == tikz

From c829e4f38c757bea55d5de5b5eda2bca722f032a Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Wed, 20 Nov 2024 12:32:01 +0000
Subject: [PATCH 31/41] change label flag as hidden by default

---
 tests/backend/test_frame_drawing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/backend/test_frame_drawing.py b/tests/backend/test_frame_drawing.py
index 9383849f..08fb0b01 100644
--- a/tests/backend/test_frame_drawing.py
+++ b/tests/backend/test_frame_drawing.py
@@ -619,7 +619,7 @@ def test_drawable_generation(diagram, drawable):
 @pytest.mark.parametrize('diagram, tikz', zip(diagrams, tikz_outputs))
 def test_tikz_drawing(diagram, tikz, capsys):
 
-    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=False, color_boxes=False)
+    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=False, color_boxes=False, draw_type_labels=True)
 
     tikz_op, _ = capsys.readouterr()
 
@@ -629,7 +629,7 @@ def test_tikz_drawing(diagram, tikz, capsys):
 @pytest.mark.parametrize('diagram, tikz', zip(diagrams, colored_tikz_outputs))
 def test_tikz_colored_drawing(diagram, tikz, capsys):
 
-    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=True, color_boxes=True)
+    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=True, color_boxes=True, draw_type_labels=True)
 
     tikz_op, _ = capsys.readouterr()
 

From 99ed5e4fe2b273410fd6867c791fad7eb54d2eea Mon Sep 17 00:00:00 2001
From: "Neil John D. Ortega" <neil.ortega@quantinuum.com>
Date: Thu, 21 Nov 2024 10:37:12 +0000
Subject: [PATCH 32/41] Fix code formatting

---
 lambeq/backend/drawing/drawing.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index 66bafd0b..106666fa 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -133,12 +133,14 @@ def draw(diagram: Diagram, **params) -> None:
         backend = TikzBackend(
             use_tikzstyles=params.get('use_tikzstyles', None),
             wires_linewidth=params.get('wires_linewidth',
-                                       WIRES_DEFAULT_WIDTH_TIKZ))
+                                       WIRES_DEFAULT_WIDTH_TIKZ)
+        )
     else:
-        backend = (
-            MatBackend(figsize=params.get('figsize', None),
-                       wires_linewidth=params.get('wires_linewidth',
-                                                  WIRES_DEFAULT_WIDTH_MAT)))
+        backend = MatBackend(
+            figsize=params.get('figsize', None),
+            wires_linewidth=params.get('wires_linewidth',
+                                       WIRES_DEFAULT_WIDTH_MAT)
+        )
 
     min_size = 0.01
     max_v = max([v for point in ([point.coordinates for point in

From 5ee51b2677dd7439d7f95fc0690d074bc17cb1b8 Mon Sep 17 00:00:00 2001
From: "Neil John D. Ortega" <neil.ortega@quantinuum.com>
Date: Thu, 21 Nov 2024 11:19:19 +0000
Subject: [PATCH 33/41] Update constants

---
 lambeq/backend/drawing/drawing.py      | 18 ++++++++++--------
 lambeq/backend/drawing/mat_backend.py  | 15 ++++++++++-----
 lambeq/backend/drawing/tikz_backend.py | 12 ++++++++----
 3 files changed, 28 insertions(+), 17 deletions(-)

diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index 106666fa..f774f02f 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -43,13 +43,15 @@
                                                     FRAME_COLORS,
                                                     WIRE_COLORS)
 from lambeq.backend.drawing.helpers import drawn_as_spider, needs_asymmetry
-from lambeq.backend.drawing.mat_backend import MatBackend
+from lambeq.backend.drawing.mat_backend import (
+    MatBackend, WIRE_LINEWIDTH as MAT_WIRE_LINEWIDTH
+)
 from lambeq.backend.drawing.text_printer import PregroupTextPrinter
-from lambeq.backend.drawing.tikz_backend import TikzBackend
+from lambeq.backend.drawing.tikz_backend import (
+    TikzBackend, WIRE_LINEWIDTH as TIKZ_WIRE_LINEWIDTH
+)
 from lambeq.backend.grammar import Box, Diagram
 
-WIRES_DEFAULT_WIDTH_MAT = 1.25
-WIRES_DEFAULT_WIDTH_TIKZ = 1.25 * 0.4  # Tikz default standard is 0.4pt
 
 if TYPE_CHECKING:
     from IPython.core.display import HTML as HTML_ty
@@ -132,14 +134,14 @@ def draw(diagram: Diagram, **params) -> None:
     elif params.get('to_tikz', False):
         backend = TikzBackend(
             use_tikzstyles=params.get('use_tikzstyles', None),
-            wires_linewidth=params.get('wires_linewidth',
-                                       WIRES_DEFAULT_WIDTH_TIKZ)
+            wire_linewidth=params.get('wire_linewidth',
+                                      TIKZ_WIRE_LINEWIDTH)
         )
     else:
         backend = MatBackend(
             figsize=params.get('figsize', None),
-            wires_linewidth=params.get('wires_linewidth',
-                                       WIRES_DEFAULT_WIDTH_MAT)
+            wire_linewidth=params.get('wire_linewidth',
+                                      MAT_WIRE_LINEWIDTH)
         )
 
     min_size = 0.01
diff --git a/lambeq/backend/drawing/mat_backend.py b/lambeq/backend/drawing/mat_backend.py
index d2ab058f..2f598b87 100644
--- a/lambeq/backend/drawing/mat_backend.py
+++ b/lambeq/backend/drawing/mat_backend.py
@@ -31,22 +31,27 @@
 from lambeq.backend.grammar import Spider
 
 
+BOX_LINEWIDTH = 1.0
+WIRE_LINEWIDTH = BOX_LINEWIDTH * 1.25
+FONTSIZE = 12
+
+
 class MatBackend(DrawingBackend):
     """ Matplotlib drawing backend. """
 
     def __init__(self,
                  axis: plt.Axes | None = None,
                  figsize: tuple | None = None,
-                 box_linewidth: float = 1,
-                 wires_linewidth: float = 1.25):
+                 box_linewidth: float = BOX_LINEWIDTH,
+                 wire_linewidth: float = WIRE_LINEWIDTH):
         self.axis = axis or plt.subplots(figsize=figsize, facecolor='white')[1]
         self.default_aspect = 'equal' if figsize is None else 'auto'
         self.box_linewidth = box_linewidth
-        self.wires_linewidth = wires_linewidth
+        self.wire_linewidth = wire_linewidth
         self.max_width: float = 0
 
     def draw_text(self, text: str, x: float, y: float, **params) -> None:
-        params['fontsize'] = params.get('fontsize', 12)
+        params['fontsize'] = params.get('fontsize', FONTSIZE)
         self.axis.text(x, y, text, **params)
         self.max_width = max(self.max_width, x)
 
@@ -112,7 +117,7 @@ def draw_wire(self,
                 ])
 
             self.axis.add_patch(PathPatch(path, facecolor='none',
-                                          linewidth=self.wires_linewidth,
+                                          linewidth=self.wire_linewidth,
                                           edgecolor=color))
 
         self.max_width = max(self.max_width, source[0], target[0])
diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index 698c137e..c8d3b640 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -30,11 +30,15 @@
 from lambeq.backend.grammar import Spider
 
 
+BOX_LINEWIDTH = 0.4
+WIRE_LINEWIDTH = BOX_LINEWIDTH * 1.25
+
+
 class TikzBackend(DrawingBackend):
     """ Tikz drawing backend. """
 
     def __init__(self, use_tikzstyles: bool = False,
-                 wires_linewidth: float = 0.4):
+                 wire_linewidth: float = WIRE_LINEWIDTH):
         self.use_tikzstyles = use_tikzstyles
         self.node_styles: list[str] = []
         self.edge_styles: list[str] = []
@@ -43,7 +47,7 @@ def __init__(self, use_tikzstyles: bool = False,
         self.edgelayer: list[str] = []
         self.label_layer: list[str] = []
         self.max_width: float = 0
-        self.wires_linewidth = wires_linewidth
+        self.wire_linewidth = wire_linewidth
 
     @staticmethod
     def format_color(color: str) -> str:
@@ -174,7 +178,7 @@ def draw_wire(self,
             # Concatenate additional styles like looseness if present
             if style:
                 wire_style += f', {style}'
-            wire_style += f', line width={self.wires_linewidth}]\n'
+            wire_style += f', line width={self.wire_linewidth}]\n'
 
             if wire_style not in self.edge_styles:
                 self.edge_styles.append(wire_style)
@@ -182,7 +186,7 @@ def draw_wire(self,
 
         else:
             wire_options = f'-, draw={self.format_wire_color(color)}'
-            wire_options += f', line width={self.wires_linewidth}'
+            wire_options += f', line width={self.wire_linewidth}'
             if style:
                 wire_options += f', {style}'
         cmd = (

From 294cd72f1ce6dd4c33d278e1cbc5611adc49761b Mon Sep 17 00:00:00 2001
From: "Neil John D. Ortega" <neil.ortega@quantinuum.com>
Date: Thu, 21 Nov 2024 11:54:36 +0000
Subject: [PATCH 34/41] Update tests for Tikz circuit

---
 tests/backend/test_circuit_drawing.py | 672 ++++++++++++++------------
 1 file changed, 358 insertions(+), 314 deletions(-)

diff --git a/tests/backend/test_circuit_drawing.py b/tests/backend/test_circuit_drawing.py
index 59c27778..819d9382 100644
--- a/tests/backend/test_circuit_drawing.py
+++ b/tests/backend/test_circuit_drawing.py
@@ -16,6 +16,7 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
     (qubit @ CX @ qubit >> qubit @ H @ qubit @ qubit
         >> qubit @ Bra(0) @ Bra(0) @ qubit >> CX >>  H @ qubit >> Bra(0) @ Bra(0)),  # Nested cups
 
+    # TODO: Doublecheck why 'U2' label is not centered
     (Controlled(X, 1) @Id(qubit) >> Controlled(X, -1) @ qubit >>
         qubit @ Controlled(X, 1) >> qubit @ Controlled(X, -1) >>
         Controlled(X, 2) >> Controlled(X, -2) >> qubit @ Controlled(U,-1) >>
@@ -25,6 +26,7 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
     (Controlled(Controlled(X, 1), 1) >> Controlled(Controlled(X,-1), 1) >>
         Controlled(Controlled(X, -1), -1)),  # Multi-controlled X
 
+    # TODO: Check wavy discard on Tikz
     (Ket(0, 1) @ MixedState() @ Encode() >>
         Measure() @ Bra(0, 1) @ Discard()),  # Initialisation and measurement
 
@@ -34,26 +36,27 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 ]
 
 
-tikz_outputs = ["""\\begin{tikzpicture}[baseline=(0.base)]
+tikz_outputs = ["""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 1.75) {};
 \\node [] (2) at (0.5, 1.75) {};
 \\node [] (3) at (0.0, 2.25) {};
-\\node [style=none, fill=white] (4) at (0, 2.0) {0};
 \\node [] (5) at (2.0, 1.75) {};
 \\node [] (6) at (3.0, 1.75) {};
 \\node [] (7) at (2.5, 2.25) {};
-\\node [style=none, fill=white] (8) at (2.5, 2.0) {0};
 \\node [] (9) at (4.5, 1.75) {};
 \\node [] (10) at (5.5, 1.75) {};
 \\node [] (11) at (5.0, 2.25) {};
-\\node [style=none, fill=white] (12) at (5.0, 2.0) {0};
 \\node [] (13) at (-0.5, 0.75) {};
 \\node [] (14) at (0.5, 0.75) {};
 \\node [] (15) at (0.5, 1.25) {};
 \\node [] (16) at (-0.5, 1.25) {};
-\\node [style=none, fill=white] (17) at (0.0, 1.0) {H};
 \\node [circle, black] (18) at (0.0, 0.0) {};
 \\node [circle, white] (19) at (2.5, 0.0) {};
 \\node [plus] (20) at (2.5, 0.0) {};
@@ -70,45 +73,56 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (31) at (2.5, -0.75) {};
 \\node [] (32) at (0.0, 1.75) {};
 \\node [] (33) at (0.0, 1.25) {};
-\\node [style=none, fill=white, right] (34) at (0.1, 1.65) {qubit};
 \\node [] (35) at (0.0, 0.75) {};
-\\node [style=none, fill=white, right] (36) at (0.1, 0.65) {qubit};
 \\node [] (37) at (2.5, 1.75) {};
-\\node [style=none, fill=white, right] (38) at (2.6, 1.65) {qubit};
-\\node [style=none, fill=white, right] (39) at (2.6, -0.35) {qubit};
 \\node [] (40) at (5.0, 1.75) {};
-\\node [style=none, fill=white, right] (41) at (5.1, 1.65) {qubit};
 \\node [] (42) at (0.0, -2.0) {};
-\\node [style=none, fill=white, right] (43) at (0.1, -0.35) {qubit};
 \\node [] (44) at (2.5, -2.0) {};
-\\node [style=none, fill=white, right] (45) at (2.6, -1.35) {qubit};
 \\node [] (46) at (5.0, -2.0) {};
-\\node [style=none, fill=white, right] (47) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (1.center);
-\\draw [-, fill={white}] (5.center) to (6.center) to (7.center) to (5.center);
-\\draw [-, fill={white}] (9.center) to (10.center) to (11.center) to (9.center);
-\\draw [-, fill={white}] (13.center) to (14.center) to (15.center) to (16.center) to (13.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (23.center) to (24.center);
-\\draw [in=180, out=0] (18.center) to (20.center);
-\\draw [in=90, out=-90] (28.center) to (29.center);
-\\draw [in=90, out=-90] (30.center) to (31.center);
-\\draw [in=180, out=0] (25.center) to (27.center);
-\\draw [in=90, out=-90] (32.center) to (33.center);
-\\draw [in=90, out=-90] (35.center) to (24.center);
-\\draw [in=90, out=-90] (37.center) to (22.center);
-\\draw [in=90, out=-90] (21.center) to (31.center);
-\\draw [in=90, out=-90] (40.center) to (29.center);
-\\draw [in=90, out=-90] (23.center) to (42.center);
-\\draw [in=90, out=-90] (30.center) to (44.center);
-\\draw [in=90, out=-90] (28.center) to (46.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (5.center) to (6.center) to (7.center) to (5.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (9.center) to (10.center) to (11.center) to (9.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (13.center) to (14.center) to (15.center) to (16.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (23.center) to (24.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (18.center) to (20.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (28.center) to (29.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (30.center) to (31.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (25.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (32.center) to (33.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (35.center) to (24.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (37.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (21.center) to (31.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (40.center) to (29.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (23.center) to (42.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (30.center) to (44.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (28.center) to (46.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (4) at (0, 2.0) {0};
+\\node [style=none] (8) at (2.5, 2.0) {0};
+\\node [style=none] (12) at (5.0, 2.0) {0};
+\\node [style=none] (17) at (0.0, 1.0) {H};
+\\node [style=none, right] (34) at (0.1, 1.65) {qubit};
+\\node [style=none, right] (36) at (0.1, 0.65) {qubit};
+\\node [style=none, right] (38) at (2.6, 1.65) {qubit};
+\\node [style=none, right] (39) at (2.6, -0.35) {qubit};
+\\node [style=none, right] (41) at (5.1, 1.65) {qubit};
+\\node [style=none, right] (43) at (0.1, -0.35) {qubit};
+\\node [style=none, right] (45) at (2.6, -1.35) {qubit};
+\\node [style=none, right] (47) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [circle, black] (1) at (2.5, 2.0) {};
@@ -122,15 +136,12 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (9) at (3.0, 0.75) {};
 \\node [] (10) at (3.0, 1.25) {};
 \\node [] (11) at (2.0, 1.25) {};
-\\node [style=none, fill=white] (12) at (2.5, 1.0) {H};
 \\node [] (13) at (2.0, 0.25) {};
 \\node [] (14) at (3.0, 0.25) {};
 \\node [] (15) at (2.5, -0.25) {};
-\\node [style=none, fill=white] (16) at (2.5, 0.0) {0};
 \\node [] (17) at (4.5, 1.25) {};
 \\node [] (18) at (5.5, 1.25) {};
 \\node [] (19) at (5.0, 0.75) {};
-\\node [style=none, fill=white] (20) at (5.0, 1.0) {0};
 \\node [circle, black] (21) at (0.0, -1.0) {};
 \\node [circle, white] (22) at (7.5, -1.0) {};
 \\node [plus] (23) at (7.5, -1.0) {};
@@ -142,66 +153,76 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (29) at (0.5, -2.25) {};
 \\node [] (30) at (0.5, -1.75) {};
 \\node [] (31) at (-0.5, -1.75) {};
-\\node [style=none, fill=white] (32) at (0.0, -2.0) {H};
 \\node [] (33) at (-0.5, -2.75) {};
 \\node [] (34) at (0.5, -2.75) {};
 \\node [] (35) at (0.0, -3.25) {};
-\\node [style=none, fill=white] (36) at (0.0, -3.0) {0};
 \\node [] (37) at (7.0, -1.75) {};
 \\node [] (38) at (8.0, -1.75) {};
 \\node [] (39) at (7.5, -2.25) {};
-\\node [style=none, fill=white] (40) at (7.5, -2.0) {0};
 \\node [] (41) at (2.5, 3.0) {};
-\\node [style=none, fill=white, right] (42) at (2.6, 3.0) {qubit};
 \\node [] (43) at (5.0, 3.0) {};
-\\node [style=none, fill=white, right] (44) at (5.1, 3.0) {qubit};
 \\node [] (45) at (2.5, 1.25) {};
-\\node [style=none, fill=white, right] (46) at (2.6, 1.65) {qubit};
 \\node [] (47) at (2.5, 0.75) {};
 \\node [] (48) at (2.5, 0.25) {};
-\\node [style=none, fill=white, right] (49) at (2.6, 0.65) {qubit};
 \\node [] (50) at (5.0, 1.25) {};
-\\node [style=none, fill=white, right] (51) at (5.1, 1.65) {qubit};
 \\node [] (52) at (0.0, 3.0) {};
-\\node [style=none, fill=white, right] (53) at (0.1, 3.0) {qubit};
 \\node [] (54) at (7.5, 3.0) {};
-\\node [style=none, fill=white, right] (55) at (7.6, 3.0) {qubit};
 \\node [] (56) at (0.0, -1.75) {};
-\\node [style=none, fill=white, right] (57) at (0.1, -1.35) {qubit};
 \\node [] (58) at (0.0, -2.25) {};
 \\node [] (59) at (0.0, -2.75) {};
-\\node [style=none, fill=white, right] (60) at (0.1, -2.35) {qubit};
 \\node [] (61) at (7.5, -1.75) {};
-\\node [style=none, fill=white, right] (62) at (7.6, -1.35) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [in=90, out=-90] (4.center) to (5.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=180, out=0] (1.center) to (3.center);
-\\draw [-, fill={white}] (8.center) to (9.center) to (10.center) to (11.center) to (8.center);
-\\draw [-, fill={white}] (13.center) to (14.center) to (15.center) to (13.center);
-\\draw [-, fill={white}] (17.center) to (18.center) to (19.center) to (17.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=-90] (26.center) to (27.center);
-\\draw [in=180, out=0] (21.center) to (23.center);
-\\draw [-, fill={white}] (28.center) to (29.center) to (30.center) to (31.center) to (28.center);
-\\draw [-, fill={white}] (33.center) to (34.center) to (35.center) to (33.center);
-\\draw [-, fill={white}] (37.center) to (38.center) to (39.center) to (37.center);
-\\draw [in=90, out=-90] (41.center) to (7.center);
-\\draw [in=90, out=-90] (43.center) to (5.center);
-\\draw [in=90, out=-90] (6.center) to (45.center);
-\\draw [in=90, out=-90] (47.center) to (48.center);
-\\draw [in=90, out=-90] (4.center) to (50.center);
-\\draw [in=90, out=-90] (52.center) to (27.center);
-\\draw [in=90, out=-90] (54.center) to (25.center);
-\\draw [in=90, out=-90] (26.center) to (56.center);
-\\draw [in=90, out=-90] (58.center) to (59.center);
-\\draw [in=90, out=-90] (24.center) to (61.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (4.center) to (5.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (6.center) to (7.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (1.center) to (3.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (8.center) to (9.center) to (10.center) to (11.center) to (8.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (13.center) to (14.center) to (15.center) to (13.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (17.center) to (18.center) to (19.center) to (17.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (24.center) to (25.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (26.center) to (27.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (21.center) to (23.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (28.center) to (29.center) to (30.center) to (31.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (33.center) to (34.center) to (35.center) to (33.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (37.center) to (38.center) to (39.center) to (37.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (41.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (43.center) to (5.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (6.center) to (45.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (47.center) to (48.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (4.center) to (50.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (52.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (54.center) to (25.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (26.center) to (56.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (58.center) to (59.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (24.center) to (61.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (12) at (2.5, 1.0) {H};
+\\node [style=none] (16) at (2.5, 0.0) {0};
+\\node [style=none] (20) at (5.0, 1.0) {0};
+\\node [style=none] (32) at (0.0, -2.0) {H};
+\\node [style=none] (36) at (0.0, -3.0) {0};
+\\node [style=none] (40) at (7.5, -2.0) {0};
+\\node [style=none, right] (42) at (2.6, 3.0) {qubit};
+\\node [style=none, right] (44) at (5.1, 3.0) {qubit};
+\\node [style=none, right] (46) at (2.6, 1.65) {qubit};
+\\node [style=none, right] (49) at (2.6, 0.65) {qubit};
+\\node [style=none, right] (51) at (5.1, 1.65) {qubit};
+\\node [style=none, right] (53) at (0.1, 3.0) {qubit};
+\\node [style=none, right] (55) at (7.6, 3.0) {qubit};
+\\node [style=none, right] (57) at (0.1, -1.35) {qubit};
+\\node [style=none, right] (60) at (0.1, -2.35) {qubit};
+\\node [style=none, right] (62) at (7.6, -1.35) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [circle, black] (1) at (0.0, 5.5) {};
@@ -255,7 +276,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (49) at (3.0, -0.75) {};
 \\node [] (50) at (3.0, -0.25) {};
 \\node [] (51) at (2.0, -0.25) {};
-\\node [style=none, fill=white] (52) at (2.5, -0.5) {U};
 \\node [] (53) at (5.0, -0.75) {};
 \\node [] (54) at (5.0, -0.25) {};
 \\node [] (55) at (3.0, -0.5) {};
@@ -264,7 +284,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (58) at (3.0, -1.75) {};
 \\node [] (59) at (3.0, -1.25) {};
 \\node [] (60) at (2.0, -1.25) {};
-\\node [style=none, fill=white] (61) at (2.5, -1.5) {U};
 \\node [] (62) at (0.0, -1.75) {};
 \\node [] (63) at (0.0, -1.25) {};
 \\node [] (64) at (2.0, -1.5) {};
@@ -273,7 +292,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (67) at (0.5, -2.75) {};
 \\node [] (68) at (0.5, -2.25) {};
 \\node [] (69) at (-0.5, -2.25) {};
-\\node [style=none, fill=white] (70) at (0.0, -2.5) {U};
 \\node [] (71) at (5.0, -2.75) {};
 \\node [] (72) at (5.0, -2.25) {};
 \\node [] (73) at (2.5, -2.75) {};
@@ -284,7 +302,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (78) at (5.5, -3.75) {};
 \\node [] (79) at (5.5, -3.25) {};
 \\node [] (80) at (4.5, -3.25) {};
-\\node [style=none, fill=white] (81) at (5.0, -3.5) {U};
 \\node [] (82) at (0.0, -3.75) {};
 \\node [] (83) at (0.0, -3.25) {};
 \\node [] (84) at (2.5, -3.75) {};
@@ -295,7 +312,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (89) at (3.0, -4.75) {};
 \\node [] (90) at (3.0, -4.25) {};
 \\node [] (91) at (-0.5, -4.25) {};
-\\node [style=none, fill=white] (92) at (2.0, -4.5) {U2};
 \\node [] (93) at (5.0, -4.75) {};
 \\node [] (94) at (5.0, -4.25) {};
 \\node [] (95) at (3.0, -4.5) {};
@@ -304,145 +320,157 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (98) at (5.5, -5.75) {};
 \\node [] (99) at (5.5, -5.25) {};
 \\node [] (100) at (2.0, -5.25) {};
-\\node [style=none, fill=white] (101) at (3.0, -5.5) {U2};
 \\node [] (102) at (0.0, -5.75) {};
 \\node [] (103) at (0.0, -5.25) {};
 \\node [] (104) at (2.0, -5.5) {};
 \\node [] (105) at (0.0, 6.5) {};
-\\node [style=none, fill=white, right] (106) at (0.1, 6.5) {qubit};
 \\node [] (107) at (2.5, 6.5) {};
-\\node [style=none, fill=white, right] (108) at (2.6, 6.5) {qubit};
-\\node [style=none, fill=white, right] (109) at (0.1, 5.15) {qubit};
-\\node [style=none, fill=white, right] (110) at (2.6, 5.15) {qubit};
-\\node [style=none, fill=white, right] (111) at (2.6, 4.15) {qubit};
 \\node [] (112) at (5.0, 6.5) {};
-\\node [style=none, fill=white, right] (113) at (5.1, 6.5) {qubit};
-\\node [style=none, fill=white, right] (114) at (2.6, 3.15) {qubit};
-\\node [style=none, fill=white, right] (115) at (5.1, 3.15) {qubit};
-\\node [style=none, fill=white, right] (116) at (0.1, 4.15) {qubit};
-\\node [style=none, fill=white, right] (117) at (2.6, 2.15) {qubit};
-\\node [style=none, fill=white, right] (118) at (5.1, 2.15) {qubit};
-\\node [style=none, fill=white, right] (119) at (0.1, 1.15) {qubit};
-\\node [style=none, fill=white, right] (120) at (2.6, 1.15) {qubit};
-\\node [style=none, fill=white, right] (121) at (5.1, 1.15) {qubit};
 \\node [] (122) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (123) at (2.6, 0.15) {qubit};
-\\node [style=none, fill=white, right] (124) at (5.1, 0.15) {qubit};
-\\node [style=none, fill=white, right] (125) at (0.1, 0.15) {qubit};
 \\node [] (126) at (2.5, -0.75) {};
 \\node [] (127) at (2.5, -1.25) {};
-\\node [style=none, fill=white, right] (128) at (2.6, -0.85) {qubit};
 \\node [] (129) at (0.0, -2.25) {};
-\\node [style=none, fill=white, right] (130) at (0.1, -1.85) {qubit};
 \\node [] (131) at (2.5, -1.75) {};
-\\node [style=none, fill=white, right] (132) at (2.6, -1.85) {qubit};
-\\node [style=none, fill=white, right] (133) at (5.1, -0.85) {qubit};
 \\node [] (134) at (0.0, -2.75) {};
-\\node [style=none, fill=white, right] (135) at (0.1, -2.85) {qubit};
-\\node [style=none, fill=white, right] (136) at (2.6, -2.85) {qubit};
 \\node [] (137) at (5.0, -3.25) {};
-\\node [style=none, fill=white, right] (138) at (5.1, -2.85) {qubit};
 \\node [] (139) at (0.0, -4.25) {};
-\\node [style=none, fill=white, right] (140) at (0.1, -3.85) {qubit};
 \\node [] (141) at (2.5, -4.25) {};
-\\node [style=none, fill=white, right] (142) at (2.6, -3.85) {qubit};
 \\node [] (143) at (5.0, -3.75) {};
-\\node [style=none, fill=white, right] (144) at (5.1, -3.85) {qubit};
 \\node [] (145) at (0.0, -4.75) {};
-\\node [style=none, fill=white, right] (146) at (0.1, -4.85) {qubit};
 \\node [] (147) at (2.5, -4.75) {};
 \\node [] (148) at (2.5, -5.25) {};
-\\node [style=none, fill=white, right] (149) at (2.6, -4.85) {qubit};
 \\node [] (150) at (5.0, -5.25) {};
-\\node [style=none, fill=white, right] (151) at (5.1, -4.85) {qubit};
 \\node [] (152) at (0.0, -6.5) {};
-\\node [style=none, fill=white, right] (153) at (0.1, -5.85) {qubit};
 \\node [] (154) at (2.5, -5.75) {};
 \\node [] (155) at (2.5, -6.5) {};
-\\node [style=none, fill=white, right] (156) at (2.6, -5.85) {qubit};
 \\node [] (157) at (5.0, -5.75) {};
 \\node [] (158) at (5.0, -6.5) {};
-\\node [style=none, fill=white, right] (159) at (5.1, -5.85) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
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-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=180, out=0] (1.center) to (3.center);
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-\\draw [in=90, out=-90] (13.center) to (14.center);
-\\draw [in=0, out=180] (8.center) to (10.center);
-\\draw [in=90, out=-90] (18.center) to (19.center);
-\\draw [in=90, out=-90] (20.center) to (21.center);
-\\draw [in=180, out=0] (15.center) to (17.center);
-\\draw [in=90, out=-90] (25.center) to (26.center);
-\\draw [in=90, out=-90] (27.center) to (28.center);
-\\draw [in=0, out=180] (22.center) to (24.center);
-\\draw [in=90, out=-90] (32.center) to (33.center);
-\\draw [in=90, out=-90] (34.center) to (35.center);
-\\draw [in=90, out=-90] (36.center) to (37.center);
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-\\draw [in=90, out=-90] (41.center) to (42.center);
-\\draw [in=90, out=-90] (43.center) to (44.center);
-\\draw [in=90, out=-90] (45.center) to (46.center);
-\\draw [in=0, out=180] (38.center) to (40.center);
-\\draw [-, fill={white}] (48.center) to (49.center) to (50.center) to (51.center) to (48.center);
-\\draw [in=90, out=-90] (53.center) to (54.center);
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-\\draw [-, fill={white}] (57.center) to (58.center) to (59.center) to (60.center) to (57.center);
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-\\draw [in=90, out=-90] (157.center) to (158.center);
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+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (6.center) to (7.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (1.center) to (3.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (11.center) to (12.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (13.center) to (14.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (8.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (18.center) to (19.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (20.center) to (21.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (15.center) to (17.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (25.center) to (26.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (27.center) to (28.center);
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+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (84.center) to (141.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (143.center) to (94.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (145.center) to (103.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (147.center) to (148.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (93.center) to (150.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (102.center) to (152.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (154.center) to (155.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (157.center) to (158.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (52) at (2.5, -0.5) {U};
+\\node [style=none] (61) at (2.5, -1.5) {U};
+\\node [style=none] (70) at (0.0, -2.5) {U};
+\\node [style=none] (81) at (5.0, -3.5) {U};
+\\node [style=none] (92) at (2.0, -4.5) {U2};
+\\node [style=none] (101) at (3.0, -5.5) {U2};
+\\node [style=none, right] (106) at (0.1, 6.5) {qubit};
+\\node [style=none, right] (108) at (2.6, 6.5) {qubit};
+\\node [style=none, right] (109) at (0.1, 5.15) {qubit};
+\\node [style=none, right] (110) at (2.6, 5.15) {qubit};
+\\node [style=none, right] (111) at (2.6, 4.15) {qubit};
+\\node [style=none, right] (113) at (5.1, 6.5) {qubit};
+\\node [style=none, right] (114) at (2.6, 3.15) {qubit};
+\\node [style=none, right] (115) at (5.1, 3.15) {qubit};
+\\node [style=none, right] (116) at (0.1, 4.15) {qubit};
+\\node [style=none, right] (117) at (2.6, 2.15) {qubit};
+\\node [style=none, right] (118) at (5.1, 2.15) {qubit};
+\\node [style=none, right] (119) at (0.1, 1.15) {qubit};
+\\node [style=none, right] (120) at (2.6, 1.15) {qubit};
+\\node [style=none, right] (121) at (5.1, 1.15) {qubit};
+\\node [style=none, right] (123) at (2.6, 0.15) {qubit};
+\\node [style=none, right] (124) at (5.1, 0.15) {qubit};
+\\node [style=none, right] (125) at (0.1, 0.15) {qubit};
+\\node [style=none, right] (128) at (2.6, -0.85) {qubit};
+\\node [style=none, right] (130) at (0.1, -1.85) {qubit};
+\\node [style=none, right] (132) at (2.6, -1.85) {qubit};
+\\node [style=none, right] (133) at (5.1, -0.85) {qubit};
+\\node [style=none, right] (135) at (0.1, -2.85) {qubit};
+\\node [style=none, right] (136) at (2.6, -2.85) {qubit};
+\\node [style=none, right] (138) at (5.1, -2.85) {qubit};
+\\node [style=none, right] (140) at (0.1, -3.85) {qubit};
+\\node [style=none, right] (142) at (2.6, -3.85) {qubit};
+\\node [style=none, right] (144) at (5.1, -3.85) {qubit};
+\\node [style=none, right] (146) at (0.1, -4.85) {qubit};
+\\node [style=none, right] (149) at (2.6, -4.85) {qubit};
+\\node [style=none, right] (151) at (5.1, -4.85) {qubit};
+\\node [style=none, right] (153) at (0.1, -5.85) {qubit};
+\\node [style=none, right] (156) at (2.6, -5.85) {qubit};
+\\node [style=none, right] (159) at (5.1, -5.85) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [circle, black] (1) at (0.0, 1.0) {};
@@ -476,77 +504,80 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (29) at (5.0, -1.25) {};
 \\node [] (30) at (5.0, -0.75) {};
 \\node [] (31) at (0.0, 2.0) {};
-\\node [style=none, fill=white, right] (32) at (0.1, 2.0) {qubit};
 \\node [] (33) at (2.5, 2.0) {};
-\\node [style=none, fill=white, right] (34) at (2.6, 2.0) {qubit};
 \\node [] (35) at (5.0, 2.0) {};
-\\node [style=none, fill=white, right] (36) at (5.1, 2.0) {qubit};
-\\node [style=none, fill=white, right] (37) at (0.1, 0.65) {qubit};
-\\node [style=none, fill=white, right] (38) at (2.6, 0.65) {qubit};
-\\node [style=none, fill=white, right] (39) at (5.1, 0.65) {qubit};
-\\node [style=none, fill=white, right] (40) at (0.1, -0.35) {qubit};
-\\node [style=none, fill=white, right] (41) at (2.6, -0.35) {qubit};
-\\node [style=none, fill=white, right] (42) at (5.1, -0.35) {qubit};
 \\node [] (43) at (0.0, -2.0) {};
-\\node [style=none, fill=white, right] (44) at (0.1, -1.35) {qubit};
 \\node [] (45) at (2.5, -2.0) {};
-\\node [style=none, fill=white, right] (46) at (2.6, -1.35) {qubit};
 \\node [] (47) at (5.0, -2.0) {};
-\\node [style=none, fill=white, right] (48) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [in=90, out=-90] (5.center) to (6.center);
-\\draw [in=90, out=-90] (7.center) to (8.center);
-\\draw [in=180, out=0] (2.center) to (4.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=180, out=0] (1.center) to (2.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=90, out=-90] (17.center) to (18.center);
-\\draw [in=0, out=180] (12.center) to (14.center);
-\\draw [in=90, out=-90] (19.center) to (20.center);
-\\draw [in=180, out=0] (11.center) to (14.center);
-\\draw [in=90, out=-90] (25.center) to (26.center);
-\\draw [in=90, out=-90] (27.center) to (28.center);
-\\draw [in=0, out=180] (22.center) to (24.center);
-\\draw [in=90, out=-90] (29.center) to (30.center);
-\\draw [in=0, out=180] (21.center) to (22.center);
-\\draw [in=90, out=-90] (31.center) to (10.center);
-\\draw [in=90, out=-90] (33.center) to (8.center);
-\\draw [in=90, out=-90] (35.center) to (6.center);
-\\draw [in=90, out=-90] (9.center) to (20.center);
-\\draw [in=90, out=-90] (7.center) to (16.center);
-\\draw [in=90, out=-90] (5.center) to (18.center);
-\\draw [in=90, out=-90] (19.center) to (26.center);
-\\draw [in=90, out=-90] (15.center) to (28.center);
-\\draw [in=90, out=-90] (17.center) to (30.center);
-\\draw [in=90, out=-90] (25.center) to (43.center);
-\\draw [in=90, out=-90] (27.center) to (45.center);
-\\draw [in=90, out=-90] (29.center) to (47.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (5.center) to (6.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (7.center) to (8.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (2.center) to (4.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (9.center) to (10.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (1.center) to (2.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (15.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (17.center) to (18.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (12.center) to (14.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (19.center) to (20.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (11.center) to (14.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (25.center) to (26.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (27.center) to (28.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (22.center) to (24.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (29.center) to (30.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (31.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (33.center) to (8.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (35.center) to (6.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (9.center) to (20.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (7.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (5.center) to (18.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (19.center) to (26.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (15.center) to (28.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (17.center) to (30.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (25.center) to (43.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (27.center) to (45.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (29.center) to (47.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none, right] (32) at (0.1, 2.0) {qubit};
+\\node [style=none, right] (34) at (2.6, 2.0) {qubit};
+\\node [style=none, right] (36) at (5.1, 2.0) {qubit};
+\\node [style=none, right] (37) at (0.1, 0.65) {qubit};
+\\node [style=none, right] (38) at (2.6, 0.65) {qubit};
+\\node [style=none, right] (39) at (5.1, 0.65) {qubit};
+\\node [style=none, right] (40) at (0.1, -0.35) {qubit};
+\\node [style=none, right] (41) at (2.6, -0.35) {qubit};
+\\node [style=none, right] (42) at (5.1, -0.35) {qubit};
+\\node [style=none, right] (44) at (0.1, -1.35) {qubit};
+\\node [style=none, right] (46) at (2.6, -1.35) {qubit};
+\\node [style=none, right] (48) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.25) {};
 \\node [] (2) at (0.5, 0.25) {};
 \\node [] (3) at (0.0, 0.75) {};
-\\node [style=none, fill=white] (4) at (0.0, 0.5) {0};
 \\node [] (5) at (2.0, 0.25) {};
 \\node [] (6) at (3.0, 0.25) {};
 \\node [] (7) at (2.5, 0.75) {};
-\\node [style=none, fill=white] (8) at (2.5, 0.5) {1};
 \\node [] (9) at (4.5, 0.25) {};
 \\node [] (10) at (5.5, 0.25) {};
 \\node [] (11) at (5.5, 0.75) {};
 \\node [] (12) at (4.5, 0.75) {};
-\\node [style=none, fill=white] (13) at (5.0, 0.5) {MixedState};
 \\node [] (14) at (7.0, 0.25) {};
 \\node [] (15) at (8.0, 0.25) {};
 \\node [] (16) at (8.0, 0.75) {};
 \\node [] (17) at (7.0, 0.75) {};
-\\node [style=none, fill=white] (18) at (7.5, 0.5) {Encode};
 \\node [] (19) at (-0.5, -0.75) {};
 \\node [] (20) at (0.5, -0.75) {};
 \\node [] (21) at (0.5, -0.25) {};
@@ -559,11 +590,9 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (28) at (2.0, -0.25) {};
 \\node [] (29) at (3.0, -0.25) {};
 \\node [] (30) at (2.5, -0.75) {};
-\\node [style=none, fill=white] (31) at (2.5, -0.5) {0};
 \\node [] (32) at (4.5, -0.25) {};
 \\node [] (33) at (5.5, -0.25) {};
 \\node [] (34) at (5.0, -0.75) {};
-\\node [style=none, fill=white] (35) at (5.0, -0.5) {1};
 \\node [] (36) at (7.0, -0.25) {};
 \\node [] (37) at (8.0, -0.25) {};
 \\node [] (38) at (7.1, -0.35) {};
@@ -572,194 +601,209 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (41) at (7.8, -0.45) {};
 \\node [] (42) at (7.5, 1.5) {};
 \\node [] (43) at (7.5, 0.75) {};
-\\node [style=none, fill=white, right] (44) at (7.6, 1.5) {bit};
 \\node [] (45) at (0.0, 0.25) {};
 \\node [] (46) at (0.0, -0.25) {};
-\\node [style=none, fill=white, right] (47) at (0.1, 0.15) {qubit};
 \\node [] (48) at (2.5, 0.25) {};
 \\node [] (49) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (50) at (2.6, 0.15) {qubit};
 \\node [] (51) at (5.0, 0.25) {};
 \\node [] (52) at (5.0, -0.25) {};
-\\node [style=none, fill=white, right] (53) at (5.1, 0.15) {qubit};
 \\node [] (54) at (7.5, 0.25) {};
 \\node [] (55) at (7.5, -0.25) {};
-\\node [style=none, fill=white, right] (56) at (7.6, 0.15) {qubit};
 \\node [] (57) at (0.0, -0.75) {};
 \\node [] (58) at (0.0, -1.5) {};
-\\node [style=none, fill=white, right] (59) at (0.1, -0.85) {bit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (1.center);
-\\draw [-, fill={white}] (5.center) to (6.center) to (7.center) to (5.center);
-\\draw [-, fill={white}] (9.center) to (10.center) to (11.center) to (12.center) to (9.center);
-\\draw [-, fill={white}] (14.center) to (15.center) to (16.center) to (17.center) to (14.center);
-\\draw [-, fill={white}] (19.center) to (20.center) to (21.center) to (22.center) to (19.center);
-\\draw [in=180, out=-90, looseness=1.26] (23.center) to (24.center);
-\\draw [in=90, out=0, looseness=1.26] (24.center) to (25.center);
-\\draw [in=90, out=-90, ->looseness=0.4118] (26.center) to (27.center);
-\\draw [-, fill={white}] (28.center) to (29.center) to (30.center) to (28.center);
-\\draw [-, fill={white}] (32.center) to (33.center) to (34.center) to (32.center);
-\\draw [in=90, out=-90] (36.center) to (37.center);
-\\draw [in=90, out=-90] (38.center) to (39.center);
-\\draw [in=90, out=-90] (40.center) to (41.center);
-\\draw [in=90, out=-90] (42.center) to (43.center);
-\\draw [in=90, out=-90] (45.center) to (46.center);
-\\draw [in=90, out=-90] (48.center) to (49.center);
-\\draw [in=90, out=-90] (51.center) to (52.center);
-\\draw [in=90, out=-90] (54.center) to (55.center);
-\\draw [in=90, out=-90] (57.center) to (58.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (5.center) to (6.center) to (7.center) to (5.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (9.center) to (10.center) to (11.center) to (12.center) to (9.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (14.center) to (15.center) to (16.center) to (17.center) to (14.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (19.center) to (20.center) to (21.center) to (22.center) to (19.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=1.26] (23.center) to (24.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=1.26] (24.center) to (25.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, ->looseness=0.4118] (26.center) to (27.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (28.center) to (29.center) to (30.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (32.center) to (33.center) to (34.center) to (32.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (36.center) to (37.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (38.center) to (39.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (40.center) to (41.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (42.center) to (43.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (45.center) to (46.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (48.center) to (49.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (51.center) to (52.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (54.center) to (55.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (57.center) to (58.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (4) at (0.0, 0.5) {0};
+\\node [style=none] (8) at (2.5, 0.5) {1};
+\\node [style=none] (13) at (5.0, 0.5) {MixedState};
+\\node [style=none] (18) at (7.5, 0.5) {Encode};
+\\node [style=none] (31) at (2.5, -0.5) {0};
+\\node [style=none] (35) at (5.0, -0.5) {1};
+\\node [style=none, right] (44) at (7.6, 1.5) {bit};
+\\node [style=none, right] (47) at (0.1, 0.15) {qubit};
+\\node [style=none, right] (50) at (2.6, 0.15) {qubit};
+\\node [style=none, right] (53) at (5.1, 0.15) {qubit};
+\\node [style=none, right] (56) at (7.6, 0.15) {qubit};
+\\node [style=none, right] (59) at (0.1, -0.85) {bit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 1.25) {};
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 \\node [] (74) at (9.5, -1.25) {};
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 \\node [] (76) at (10.0, -1.75) {};
-\\node [style=none, fill=white] (77) at (10.0, -1.5) {0};
 \\node [] (78) at (0.0, 1.25) {};
 \\node [] (79) at (0.0, 0.75) {};
-\\node [style=none, fill=white, right] (80) at (0.1, 1.15) {qubit};
 \\node [] (81) at (2.5, 1.25) {};
 \\node [] (82) at (2.5, 0.75) {};
-\\node [style=none, fill=white, right] (83) at (2.6, 1.15) {qubit};
 \\node [] (84) at (7.5, 1.25) {};
 \\node [] (85) at (7.5, 0.75) {};
-\\node [style=none, fill=white, right] (86) at (7.6, 1.15) {qubit};
 \\node [] (87) at (10.0, 1.25) {};
 \\node [] (88) at (10.0, 0.75) {};
-\\node [style=none, fill=white, right] (89) at (10.1, 1.15) {qubit};
 \\node [] (90) at (0.0, 0.25) {};
 \\node [] (91) at (0.0, -0.25) {};
-\\node [style=none, fill=white, right] (92) at (0.1, 0.15) {qubit};
 \\node [] (93) at (2.5, 0.25) {};
 \\node [] (94) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (95) at (2.6, 0.15) {qubit};
 \\node [] (96) at (7.5, 0.25) {};
 \\node [] (97) at (7.5, -0.25) {};
-\\node [style=none, fill=white, right] (98) at (7.6, 0.15) {qubit};
 \\node [] (99) at (10.0, 0.25) {};
 \\node [] (100) at (10.0, -0.25) {};
-\\node [style=none, fill=white, right] (101) at (10.1, 0.15) {qubit};
 \\node [] (102) at (0.0, -0.75) {};
 \\node [] (103) at (0.0, -1.25) {};
-\\node [style=none, fill=white, right] (104) at (0.1, -0.85) {qubit};
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 \\node [] (106) at (2.5, -1.25) {};
-\\node [style=none, fill=white, right] (107) at (2.6, -0.85) {qubit};
 \\node [] (108) at (7.5, -0.75) {};
 \\node [] (109) at (7.5, -1.25) {};
-\\node [style=none, fill=white, right] (110) at (7.6, -0.85) {qubit};
 \\node [] (111) at (10.0, -0.75) {};
 \\node [] (112) at (10.0, -1.25) {};
-\\node [style=none, fill=white, right] (113) at (10.1, -0.85) {qubit};
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-\\draw [in=90, out=-90] (108.center) to (109.center);
-\\draw [in=90, out=-90] (111.center) to (112.center);
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+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (47.center) to (48.center) to (49.center) to (50.center) to (47.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (52.center) to (53.center) to (54.center) to (55.center) to (52.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (57.center) to (58.center) to (59.center) to (60.center) to (57.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (62.center) to (63.center) to (64.center) to (62.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (66.center) to (67.center) to (68.center) to (66.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (70.center) to (71.center) to (72.center) to (70.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (74.center) to (75.center) to (76.center) to (74.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (78.center) to (79.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (81.center) to (82.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (84.center) to (85.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (87.center) to (88.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (90.center) to (91.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (93.center) to (94.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (96.center) to (97.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (99.center) to (100.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (102.center) to (103.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (105.center) to (106.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (108.center) to (109.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (111.center) to (112.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (4) at (0.0, 1.5) {0};
+\\node [style=none] (8) at (2.5, 1.5) {0};
+\\node [style=none] (12) at (7.5, 1.5) {0};
+\\node [style=none] (16) at (10.0, 1.5) {0};
+\\node [style=none] (21) at (0.0, 0.5) {S};
+\\node [style=none] (26) at (2.5, 0.5) {X};
+\\node [style=none] (31) at (7.5, 0.5) {Y};
+\\node [style=none] (36) at (10.0, 0.5) {Z};
+\\node [style=none] (41) at (0.0, -0.5) {Rx(0.3)};
+\\node [style=none] (46) at (2.5, -0.5) {Ry(0.2)};
+\\node [style=none] (51) at (5.0, 1.5) {0.500};
+\\node [style=none] (56) at (7.5, -0.5) {Rz(0.1)};
+\\node [style=none] (61) at (10.0, -0.5) {H};
+\\node [style=none] (65) at (0.0, -1.5) {0};
+\\node [style=none] (69) at (2.5, -1.5) {0};
+\\node [style=none] (73) at (7.5, -1.5) {0};
+\\node [style=none] (77) at (10.0, -1.5) {0};
+\\node [style=none, right] (80) at (0.1, 1.15) {qubit};
+\\node [style=none, right] (83) at (2.6, 1.15) {qubit};
+\\node [style=none, right] (86) at (7.6, 1.15) {qubit};
+\\node [style=none, right] (89) at (10.1, 1.15) {qubit};
+\\node [style=none, right] (92) at (0.1, 0.15) {qubit};
+\\node [style=none, right] (95) at (2.6, 0.15) {qubit};
+\\node [style=none, right] (98) at (7.6, 0.15) {qubit};
+\\node [style=none, right] (101) at (10.1, 0.15) {qubit};
+\\node [style=none, right] (104) at (0.1, -0.85) {qubit};
+\\node [style=none, right] (107) at (2.6, -0.85) {qubit};
+\\node [style=none, right] (110) at (7.6, -0.85) {qubit};
+\\node [style=none, right] (113) at (10.1, -0.85) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 

From 02b35f3b7d206ead11d151b6744adce875e0fe7b Mon Sep 17 00:00:00 2001
From: "Neil John D. Ortega" <neil.ortega@quantinuum.com>
Date: Thu, 21 Nov 2024 12:04:40 +0000
Subject: [PATCH 35/41] Update tests for Tikz diagrams

---
 tests/backend/test_drawing.py | 290 +++++++++++++++++++---------------
 1 file changed, 166 insertions(+), 124 deletions(-)

diff --git a/tests/backend/test_drawing.py b/tests/backend/test_drawing.py
index e2146245..89d4b6e0 100644
--- a/tests/backend/test_drawing.py
+++ b/tests/backend/test_drawing.py
@@ -188,201 +188,236 @@
 
 
 tikz_outputs = [
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, -0.25) {};
 \\node [] (2) at (3.0, -0.25) {};
 \\node [] (3) at (3.0, 0.25) {};
 \\node [] (4) at (-0.5, 0.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 0.0) {BX1};
 \\node [] (6) at (1.25, 1.0) {};
 \\node [] (7) at (1.25, 0.25) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 1.0) {s};
 \\node [] (9) at (0.0, -0.25) {};
 \\node [] (10) at (0.0, -1.0) {};
-\\node [style=none, fill=white, right] (11) at (0.1, -0.35) {s};
 \\node [] (12) at (2.5, -0.25) {};
 \\node [] (13) at (2.5, -1.0) {};
-\\node [style=none, fill=white, right] (14) at (2.6, -0.35) {s};
 \\node [] (15) at (5.0, 1.0) {};
 \\node [] (16) at (5.0, -1.0) {};
-\\node [style=none, fill=white, right] (17) at (5.1, 1.0) {s};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (15.center) to (16.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 0.0) {BX1};
+\\node [style=none, right] (8) at (1.35, 1.0) {s};
+\\node [style=none, right] (11) at (0.1, -0.35) {s};
+\\node [style=none, right] (14) at (2.6, -0.35) {s};
+\\node [style=none, right] (17) at (5.1, 1.0) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.25) {};
 \\node [] (2) at (3.0, 0.25) {};
 \\node [] (3) at (3.25, 0.75) {};
 \\node [] (4) at (-0.5, 0.75) {};
-\\node [style=none, fill=white] (5) at (1.25, 0.5) {BX1};
 \\node [] (6) at (-0.5, -0.75) {};
 \\node [] (7) at (3.25, -0.75) {};
 \\node [] (8) at (3.0, -0.25) {};
 \\node [] (9) at (-0.5, -0.25) {};
-\\node [style=none, fill=white] (10) at (1.25, -0.5) {BX1†};
 \\node [] (11) at (1.25, 1.5) {};
 \\node [] (12) at (1.25, 0.75) {};
-\\node [style=none, fill=white, right] (13) at (1.35, 1.5) {s};
 \\node [] (14) at (0.0, 0.25) {};
 \\node [] (15) at (0.0, -0.25) {};
-\\node [style=none, fill=white, right] (16) at (0.1, 0.15) {s};
 \\node [] (17) at (2.5, 0.25) {};
 \\node [] (18) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (19) at (2.6, 0.15) {s};
 \\node [] (20) at (1.25, -0.75) {};
 \\node [] (21) at (1.25, -1.5) {};
-\\node [style=none, fill=white, right] (22) at (1.35, -0.85) {s};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [-, fill={white}] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
-\\draw [in=90, out=-90] (11.center) to (12.center);
-\\draw [in=90, out=-90] (14.center) to (15.center);
-\\draw [in=90, out=-90] (17.center) to (18.center);
-\\draw [in=90, out=-90] (20.center) to (21.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (11.center) to (12.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (14.center) to (15.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (17.center) to (18.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (20.center) to (21.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 0.5) {BX1};
+\\node [style=none] (10) at (1.25, -0.5) {BX1†};
+\\node [style=none, right] (13) at (1.35, 1.5) {s};
+\\node [style=none, right] (16) at (0.1, 0.15) {s};
+\\node [style=none, right] (19) at (2.6, 0.15) {s};
+\\node [style=none, right] (22) at (1.35, -0.85) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.25) {};
 \\node [] (2) at (3.0, 0.25) {};
 \\node [] (3) at (3.0, 0.75) {};
 \\node [] (4) at (-0.5, 0.75) {};
-\\node [style=none, fill=white] (5) at (1.25, 0.5) {BX1};
 \\node [] (6) at (1.25, 1.5) {};
 \\node [] (7) at (1.25, 0.75) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 1.5) {s};
 \\node [] (9) at (2.5, 0.25) {};
 \\node [] (10) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (11) at (2.6, 0.15) {s};
 \\node [] (12) at (5.0, 1.5) {};
 \\node [] (13) at (5.0, -0.25) {};
-\\node [style=none, fill=white, right] (14) at (5.1, 1.5) {s.r};
 \\node [] (15) at (0.0, 0.25) {};
 \\node [] (16) at (0.0, -1.5) {};
-\\node [style=none, fill=white, right] (17) at (0.1, 0.15) {s};
 \\node [] (18) at (3.75, -0.5) {};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=180, out=-90, looseness=0.4118] (10.center) to (18.center);
-\\draw [in=0, out=-90, looseness=0.4118] (13.center) to (18.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (15.center) to (16.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (10.center) to (18.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (13.center) to (18.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 0.5) {BX1};
+\\node [style=none, right] (8) at (1.35, 1.5) {s};
+\\node [style=none, right] (11) at (2.6, 0.15) {s};
+\\node [style=none, right] (14) at (5.1, 1.5) {s.r};
+\\node [style=none, right] (17) at (0.1, 0.15) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.75) {};
 \\node [] (2) at (3.0, 0.75) {};
 \\node [] (3) at (3.0, 1.25) {};
 \\node [] (4) at (-0.5, 1.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 1.0) {BX2};
 \\node [] (6) at (1.25, 2.0) {};
 \\node [] (7) at (1.25, 1.25) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 2.0) {s};
 \\node [] (9) at (2.5, 0.75) {};
 \\node [] (10) at (2.5, 0.25) {};
-\\node [style=none, fill=white, right] (11) at (2.6, 0.65) {s.r};
 \\node [] (12) at (5.0, 2.0) {};
 \\node [] (13) at (5.0, 0.25) {};
-\\node [style=none, fill=white, right] (14) at (5.1, 2.0) {s.r};
 \\node [] (15) at (7.5, 2.0) {};
 \\node [] (16) at (7.5, 0.25) {};
-\\node [style=none, fill=white, right] (17) at (7.6, 2.0) {s.r};
 \\node [] (18) at (0.0, 0.75) {};
 \\node [] (19) at (0.0, -0.75) {};
-\\node [style=none, fill=white, right] (20) at (0.1, 0.65) {s};
 \\node [] (21) at (3.75, -0.25) {};
 \\node [] (22) at (3.75, -0.75) {};
-\\node [style=none, fill=white, right] (23) at (3.85, -0.35) {s.r};
 \\node [] (24) at (6.25, -0.25) {};
 \\node [] (25) at (6.25, -2.0) {};
-\\node [style=none, fill=white, right] (26) at (6.35, -0.35) {s.r};
 \\node [circle, fill=black, scale=0.365] (27) at (5.0, 0.0) {};
 \\node [] (28) at (1.875, -1.0) {};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=90, out=-90] (18.center) to (19.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=180, looseness=0.4118] (27.center) to (21.center);
-\\draw [in=90, out=0, looseness=0.4118] (27.center) to (24.center);
-\\draw [in=180, out=-90, looseness=0.209] (10.center) to (27.center);
-\\draw [in=90, out=-90] (13.center) to (27.center);
-\\draw [in=0, out=-90, looseness=0.209] (16.center) to (27.center);
-\\draw [in=180, out=-90, looseness=0.2775] (19.center) to (28.center);
-\\draw [in=0, out=-90, looseness=0.2775] (22.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (15.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (18.center) to (19.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (24.center) to (25.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (27.center) to (21.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (27.center) to (24.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.209] (10.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (13.center) to (27.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.209] (16.center) to (27.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.2775] (19.center) to (28.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.2775] (22.center) to (28.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 1.0) {BX2};
+\\node [style=none, right] (8) at (1.35, 2.0) {s};
+\\node [style=none, right] (11) at (2.6, 0.65) {s.r};
+\\node [style=none, right] (14) at (5.1, 2.0) {s.r};
+\\node [style=none, right] (17) at (7.6, 2.0) {s.r};
+\\node [style=none, right] (20) at (0.1, 0.65) {s};
+\\node [style=none, right] (23) at (3.85, -0.35) {s.r};
+\\node [style=none, right] (26) at (6.35, -0.35) {s.r};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (0.75, 0.75) {};
 \\node [] (2) at (1.75, 0.75) {};
 \\node [] (3) at (2.0, 1.25) {};
 \\node [] (4) at (0.75, 1.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 1.0) {BX3};
 \\node [] (6) at (-0.5, -0.25) {};
 \\node [] (7) at (3.0, -0.25) {};
 \\node [] (8) at (3.25, 0.25) {};
 \\node [] (9) at (-0.5, 0.25) {};
-\\node [style=none, fill=white] (10) at (1.25, 0.0) {BX1};
 \\node [] (11) at (2.0, -1.25) {};
 \\node [] (12) at (3.25, -1.25) {};
 \\node [] (13) at (3.0, -0.75) {};
 \\node [] (14) at (2.0, -0.75) {};
-\\node [style=none, fill=white] (15) at (2.5, -1.0) {BX3†};
 \\node [] (16) at (-0.5, -1.25) {};
 \\node [] (17) at (0.75, -1.25) {};
 \\node [] (18) at (0.5, -0.75) {};
 \\node [] (19) at (-0.5, -0.75) {};
-\\node [style=none, fill=white] (20) at (0.0, -1.0) {BX3†};
 \\node [] (21) at (1.25, 0.75) {};
 \\node [] (22) at (1.25, 0.25) {};
-\\node [style=none, fill=white, right] (23) at (1.35, 0.65) {s};
 \\node [] (24) at (2.5, -0.25) {};
 \\node [] (25) at (2.5, -0.75) {};
-\\node [style=none, fill=white, right] (26) at (2.6, -0.35) {s};
 \\node [] (27) at (0.0, -0.25) {};
 \\node [] (28) at (0.0, -0.75) {};
-\\node [style=none, fill=white, right] (29) at (0.1, -0.35) {s};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [-, fill={white}] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
-\\draw [-, fill={white}] (11.center) to (12.center) to (13.center) to (14.center) to (11.center);
-\\draw [-, fill={white}] (16.center) to (17.center) to (18.center) to (19.center) to (16.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=-90] (27.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (11.center) to (12.center) to (13.center) to (14.center) to (11.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (16.center) to (17.center) to (18.center) to (19.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (24.center) to (25.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (27.center) to (28.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 1.0) {BX3};
+\\node [style=none] (10) at (1.25, 0.0) {BX1};
+\\node [style=none] (15) at (2.5, -1.0) {BX3†};
+\\node [style=none] (20) at (0.0, -1.0) {BX3†};
+\\node [style=none, right] (23) at (1.35, 0.65) {s};
+\\node [style=none, right] (26) at (2.6, -0.35) {s};
+\\node [style=none, right] (29) at (0.1, -0.35) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
@@ -498,98 +533,105 @@ def test_tikz_drawing(diagram, tikz, capsys):
     assert tikz_op == tikz
 
 
-expected_equation_tikz = """\\begin{tikzpicture}[baseline=(0.base)]
+expected_equation_tikz = """% When embedding into a *.tex file, uncomment and include the following lines:
+% \pgfdeclarelayer{nodelayer}
+% \pgfdeclarelayer{edgelayer}
+% \pgfdeclarelayer{labellayer}
+% \pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.75) {};
 \\node [] (2) at (3.0, 0.75) {};
 \\node [] (3) at (3.25, 1.25) {};
 \\node [] (4) at (-0.5, 1.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 1.0) {BX2};
 \\node [] (6) at (1.25, 2.0) {};
 \\node [] (7) at (1.25, 1.25) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 2.0) {s};
 \\node [] (9) at (2.5, 0.75) {};
 \\node [] (10) at (2.5, 0.25) {};
-\\node [style=none, fill=white, right] (11) at (2.6, 0.65) {s.r};
 \\node [] (12) at (5.0, 2.0) {};
 \\node [] (13) at (5.0, 0.25) {};
-\\node [style=none, fill=white, right] (14) at (5.1, 2.0) {s.r};
 \\node [] (15) at (7.5, 2.0) {};
 \\node [] (16) at (7.5, 0.25) {};
-\\node [style=none, fill=white, right] (17) at (7.6, 2.0) {s.r};
 \\node [] (18) at (0.0, 0.75) {};
 \\node [] (19) at (0.0, -0.75) {};
-\\node [style=none, fill=white, right] (20) at (0.1, 0.65) {s};
 \\node [] (21) at (3.75, -0.25) {};
 \\node [] (22) at (3.75, -0.75) {};
-\\node [style=none, fill=white, right] (23) at (3.85, -0.35) {s.r};
 \\node [] (24) at (6.25, -0.25) {};
 \\node [] (25) at (6.25, -2.0) {};
-\\node [style=none, fill=white, right] (26) at (6.35, -0.35) {s.r};
 \\node [circle, fill=black, scale=0.365] (27) at (5.0, 0.0) {};
 \\node [] (28) at (1.875, -1.0) {};
-\\node [style=none, fill=white] (29) at (8.6, 0) {=};
 \\node [] (30) at (9.1, -1.25) {};
 \\node [] (31) at (12.85, -1.25) {};
 \\node [] (32) at (12.6, -0.75) {};
 \\node [] (33) at (9.1, -0.75) {};
-\\node [style=none, fill=white] (34) at (10.85, -1.0) {BX2†};
 \\node [] (35) at (13.35, 0.75) {};
 \\node [] (36) at (13.35, 0.25) {};
-\\node [style=none, fill=white, right] (37) at (13.45, 0.65) {s.r};
 \\node [] (38) at (15.85, 2.0) {};
 \\node [] (39) at (15.85, 0.25) {};
-\\node [style=none, fill=white, right] (40) at (15.95, 2.0) {s.r};
 \\node [] (41) at (9.6, 0.75) {};
 \\node [] (42) at (9.6, -0.75) {};
-\\node [style=none, fill=white, right] (43) at (9.7, 0.65) {s};
 \\node [] (44) at (12.1, -0.25) {};
 \\node [] (45) at (12.1, -0.75) {};
-\\node [style=none, fill=white, right] (46) at (12.2, -0.35) {s.r};
 \\node [] (47) at (10.85, -1.25) {};
 \\node [] (48) at (10.85, -2.0) {};
-\\node [style=none, fill=white, right] (49) at (10.95, -1.35) {s};
 \\node [] (50) at (14.6, -0.25) {};
 \\node [] (51) at (14.6, -2.0) {};
-\\node [style=none, fill=white, right] (52) at (14.7, -0.35) {s.r};
 \\node [] (53) at (17.1, -0.25) {};
 \\node [] (54) at (17.1, -2.0) {};
-\\node [style=none, fill=white, right] (55) at (17.200000000000003, -0.35) {s.r};
 \\node [] (56) at (12.1, 1.0) {};
 \\node [circle, fill=black, scale=0.242] (57) at (14.6, 0.0) {};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=90, out=-90] (18.center) to (19.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=180, looseness=0.4118] (27.center) to (21.center);
-\\draw [in=90, out=0, looseness=0.4118] (27.center) to (24.center);
-\\draw [in=180, out=-90, looseness=0.209] (10.center) to (27.center);
-\\draw [in=90, out=-90] (13.center) to (27.center);
-\\draw [in=0, out=-90, looseness=0.209] (16.center) to (27.center);
-\\draw [in=180, out=-90, looseness=0.2775] (19.center) to (28.center);
-\\draw [in=0, out=-90, looseness=0.2775] (22.center) to (28.center);
-\\draw [-, fill={white}] (30.center) to (31.center) to (32.center) to (33.center) to (30.center);
-\\draw [in=90, out=-90] (35.center) to (36.center);
-\\draw [in=90, out=-90] (38.center) to (39.center);
-\\draw [in=90, out=-90] (41.center) to (42.center);
-\\draw [in=90, out=-90] (44.center) to (45.center);
-\\draw [in=90, out=-90] (47.center) to (48.center);
-\\draw [in=90, out=-90] (50.center) to (51.center);
-\\draw [in=90, out=-90] (53.center) to (54.center);
-\\draw [in=90, out=180, looseness=0.209] (56.center) to (41.center);
-\\draw [in=90, out=0, looseness=0.4118] (56.center) to (35.center);
-\\draw [in=90, out=180, looseness=0.209] (57.center) to (44.center);
-\\draw [in=90, out=-90] (57.center) to (50.center);
-\\draw [in=90, out=0, looseness=0.209] (57.center) to (53.center);
-\\draw [in=180, out=-90, looseness=0.4118] (36.center) to (57.center);
-\\draw [in=0, out=-90, looseness=0.4118] (39.center) to (57.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (15.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (18.center) to (19.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (24.center) to (25.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (27.center) to (21.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (27.center) to (24.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.209] (10.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (13.center) to (27.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.209] (16.center) to (27.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.2775] (19.center) to (28.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.2775] (22.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (30.center) to (31.center) to (32.center) to (33.center) to (30.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (35.center) to (36.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (38.center) to (39.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (41.center) to (42.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (44.center) to (45.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (47.center) to (48.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (50.center) to (51.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (53.center) to (54.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.209] (56.center) to (41.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (56.center) to (35.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.209] (57.center) to (44.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (57.center) to (50.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.209] (57.center) to (53.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (36.center) to (57.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5, looseness=0.4118] (39.center) to (57.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 1.0) {BX2};
+\\node [style=none, right] (8) at (1.35, 2.0) {s};
+\\node [style=none, right] (11) at (2.6, 0.65) {s.r};
+\\node [style=none, right] (14) at (5.1, 2.0) {s.r};
+\\node [style=none, right] (17) at (7.6, 2.0) {s.r};
+\\node [style=none, right] (20) at (0.1, 0.65) {s};
+\\node [style=none, right] (23) at (3.85, -0.35) {s.r};
+\\node [style=none, right] (26) at (6.35, -0.35) {s.r};
+\\node [style=none] (29) at (8.6, 0) {=};
+\\node [style=none] (34) at (10.85, -1.0) {BX2†};
+\\node [style=none, right] (37) at (13.45, 0.65) {s.r};
+\\node [style=none, right] (40) at (15.95, 2.0) {s.r};
+\\node [style=none, right] (43) at (9.7, 0.65) {s};
+\\node [style=none, right] (46) at (12.2, -0.35) {s.r};
+\\node [style=none, right] (49) at (10.95, -1.35) {s};
+\\node [style=none, right] (52) at (14.7, -0.35) {s.r};
+\\node [style=none, right] (55) at (17.200000000000003, -0.35) {s.r};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 

From 697a0ee3a95ec71656015a2d14379ad8a1694bf6 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 21 Nov 2024 16:34:33 +0000
Subject: [PATCH 36/41] add box_linewidth to Tikz backend

---
 lambeq/backend/drawing/drawing.py      |  6 ++++--
 lambeq/backend/drawing/tikz_backend.py | 12 ++++++++----
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index f774f02f..12075273 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -48,7 +48,8 @@
 )
 from lambeq.backend.drawing.text_printer import PregroupTextPrinter
 from lambeq.backend.drawing.tikz_backend import (
-    TikzBackend, WIRE_LINEWIDTH as TIKZ_WIRE_LINEWIDTH
+    TikzBackend, WIRE_LINEWIDTH as TIKZ_WIRE_LINEWIDTH,
+    BOX_LINEWIDTH as TIKZ_BOX_LINEWIDTH
 )
 from lambeq.backend.grammar import Box, Diagram
 
@@ -135,7 +136,8 @@ def draw(diagram: Diagram, **params) -> None:
         backend = TikzBackend(
             use_tikzstyles=params.get('use_tikzstyles', None),
             wire_linewidth=params.get('wire_linewidth',
-                                      TIKZ_WIRE_LINEWIDTH)
+                                      TIKZ_WIRE_LINEWIDTH),
+            box_linewidth=params.get('box_linewidth', TIKZ_BOX_LINEWIDTH)
         )
     else:
         backend = MatBackend(
diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index c8d3b640..f6d54d96 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -38,7 +38,8 @@ class TikzBackend(DrawingBackend):
     """ Tikz drawing backend. """
 
     def __init__(self, use_tikzstyles: bool = False,
-                 wire_linewidth: float = WIRE_LINEWIDTH):
+                 wire_linewidth: float = WIRE_LINEWIDTH,
+                 box_linewidth: float = BOX_LINEWIDTH):
         self.use_tikzstyles = use_tikzstyles
         self.node_styles: list[str] = []
         self.edge_styles: list[str] = []
@@ -48,6 +49,7 @@ def __init__(self, use_tikzstyles: bool = False,
         self.label_layer: list[str] = []
         self.max_width: float = 0
         self.wire_linewidth = wire_linewidth
+        self.box_linewidth = box_linewidth
 
     @staticmethod
     def format_color(color: str) -> str:
@@ -129,12 +131,14 @@ def draw_polygon(self, *points: list[float], color: str = 'white') -> None:
             color_name = color.lstrip('#')
             style_name = 'box' if color == 'white' else f'{color_name}_box'
             style = (f'\\tikzstyle{{{style_name}}}='
-                     f'[-, fill={self.format_color(color)}]\n')
+                     f'[-, fill={self.format_color(color)}')
+            style += f', line width={self.box_linewidth}pt]\n'
             if style not in self.edge_styles:
                 self.edge_styles.append(style)
             options = f'style={style_name}'
         else:
             options = f'-, fill={self.format_color(color)}'
+            options += f', line width={self.box_linewidth}pt'
 
         str_connections = ' to '.join(f'({node}.center)' for node in nodes)
         self.edgelayer.append(f'\\draw [{options}] {str_connections};\n')
@@ -178,7 +182,7 @@ def draw_wire(self,
             # Concatenate additional styles like looseness if present
             if style:
                 wire_style += f', {style}'
-            wire_style += f', line width={self.wire_linewidth}]\n'
+            wire_style += f', line width={self.wire_linewidth}pt]\n'
 
             if wire_style not in self.edge_styles:
                 self.edge_styles.append(wire_style)
@@ -186,7 +190,7 @@ def draw_wire(self,
 
         else:
             wire_options = f'-, draw={self.format_wire_color(color)}'
-            wire_options += f', line width={self.wire_linewidth}'
+            wire_options += f', line width={self.wire_linewidth}pt'
             if style:
                 wire_options += f', {style}'
         cmd = (

From 5d18c22da21d8535a92d93a35a6d19b7b618ddc5 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 21 Nov 2024 16:37:42 +0000
Subject: [PATCH 37/41] update frame-drawing tests with linewidth changes

---
 tests/backend/test_frame_drawing.py | 293 +++++++++++++---------------
 1 file changed, 134 insertions(+), 159 deletions(-)

diff --git a/tests/backend/test_frame_drawing.py b/tests/backend/test_frame_drawing.py
index 08fb0b01..d3e23053 100644
--- a/tests/backend/test_frame_drawing.py
+++ b/tests/backend/test_frame_drawing.py
@@ -294,48 +294,48 @@
 \\node [] (156) at (6.5, -11.875) {};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
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-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (11.center) to (12.center) to (13.center) to (14.center) to (11.center);
-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (16.center) to (17.center) to (18.center) to (19.center) to (16.center);
-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (21.center) to (22.center) to (23.center) to (24.center) to (21.center);
-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (26.center) to (27.center) to (28.center) to (29.center) to (26.center);
-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (31.center) to (32.center) to (33.center) to (34.center) to (31.center);
-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (35.center) to (36.center) to (37.center) to (38.center) to (35.center);
-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (40.center) to (41.center) to (42.center) to (43.center) to (40.center);
-\\draw [-, fill={rgb,255: red,255; green,255; blue,255}] (44.center) to (45.center) to (46.center) to (47.center) to (44.center);
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-\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5] (155.center) to (156.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=2pt] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=2pt] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
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+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=2pt] (21.center) to (22.center) to (23.center) to (24.center) to (21.center);
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+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=2pt] (40.center) to (41.center) to (42.center) to (43.center) to (40.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=2pt] (44.center) to (45.center) to (46.center) to (47.center) to (44.center);
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 \\node [style=none] (5) at (2.75, 11.875) {Alice};
@@ -571,31 +571,6 @@
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@@ -619,7 +594,7 @@ def test_drawable_generation(diagram, drawable):
 @pytest.mark.parametrize('diagram, tikz', zip(diagrams, tikz_outputs))
 def test_tikz_drawing(diagram, tikz, capsys):
 
-    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=False, color_boxes=False, draw_type_labels=True)
+    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wire_linewidth= 3, box_linewidth=2), color_wires=False, color_boxes=False, draw_type_labels=True)
 
     tikz_op, _ = capsys.readouterr()
 
@@ -629,7 +604,7 @@ def test_tikz_drawing(diagram, tikz, capsys):
 @pytest.mark.parametrize('diagram, tikz', zip(diagrams, colored_tikz_outputs))
 def test_tikz_colored_drawing(diagram, tikz, capsys):
 
-    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wires_linewidth= 1.25 * 0.4 ), color_wires=True, color_boxes=True, draw_type_labels=True)
+    diagram.draw(backend=TikzBackend(use_tikzstyles=False, wire_linewidth=3, box_linewidth=2),  use_tikzstyles=False, foliated=False, color_wires=True, color_boxes=True)
 
     tikz_op, _ = capsys.readouterr()
 

From 93a84bb299e711c89968ab6d461e67cb7e485667 Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 21 Nov 2024 16:37:58 +0000
Subject: [PATCH 38/41] update test-drawing tests with linewidth changes

---
 tests/backend/test_drawing.py | 290 +++++++++++++++++++---------------
 1 file changed, 166 insertions(+), 124 deletions(-)

diff --git a/tests/backend/test_drawing.py b/tests/backend/test_drawing.py
index e2146245..7ed551e2 100644
--- a/tests/backend/test_drawing.py
+++ b/tests/backend/test_drawing.py
@@ -188,201 +188,236 @@
 
 
 tikz_outputs = [
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, -0.25) {};
 \\node [] (2) at (3.0, -0.25) {};
 \\node [] (3) at (3.0, 0.25) {};
 \\node [] (4) at (-0.5, 0.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 0.0) {BX1};
 \\node [] (6) at (1.25, 1.0) {};
 \\node [] (7) at (1.25, 0.25) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 1.0) {s};
 \\node [] (9) at (0.0, -0.25) {};
 \\node [] (10) at (0.0, -1.0) {};
-\\node [style=none, fill=white, right] (11) at (0.1, -0.35) {s};
 \\node [] (12) at (2.5, -0.25) {};
 \\node [] (13) at (2.5, -1.0) {};
-\\node [style=none, fill=white, right] (14) at (2.6, -0.35) {s};
 \\node [] (15) at (5.0, 1.0) {};
 \\node [] (16) at (5.0, -1.0) {};
-\\node [style=none, fill=white, right] (17) at (5.1, 1.0) {s};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (15.center) to (16.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 0.0) {BX1};
+\\node [style=none, right] (8) at (1.35, 1.0) {s};
+\\node [style=none, right] (11) at (0.1, -0.35) {s};
+\\node [style=none, right] (14) at (2.6, -0.35) {s};
+\\node [style=none, right] (17) at (5.1, 1.0) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.25) {};
 \\node [] (2) at (3.0, 0.25) {};
 \\node [] (3) at (3.25, 0.75) {};
 \\node [] (4) at (-0.5, 0.75) {};
-\\node [style=none, fill=white] (5) at (1.25, 0.5) {BX1};
 \\node [] (6) at (-0.5, -0.75) {};
 \\node [] (7) at (3.25, -0.75) {};
 \\node [] (8) at (3.0, -0.25) {};
 \\node [] (9) at (-0.5, -0.25) {};
-\\node [style=none, fill=white] (10) at (1.25, -0.5) {BX1†};
 \\node [] (11) at (1.25, 1.5) {};
 \\node [] (12) at (1.25, 0.75) {};
-\\node [style=none, fill=white, right] (13) at (1.35, 1.5) {s};
 \\node [] (14) at (0.0, 0.25) {};
 \\node [] (15) at (0.0, -0.25) {};
-\\node [style=none, fill=white, right] (16) at (0.1, 0.15) {s};
 \\node [] (17) at (2.5, 0.25) {};
 \\node [] (18) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (19) at (2.6, 0.15) {s};
 \\node [] (20) at (1.25, -0.75) {};
 \\node [] (21) at (1.25, -1.5) {};
-\\node [style=none, fill=white, right] (22) at (1.35, -0.85) {s};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [-, fill={white}] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
-\\draw [in=90, out=-90] (11.center) to (12.center);
-\\draw [in=90, out=-90] (14.center) to (15.center);
-\\draw [in=90, out=-90] (17.center) to (18.center);
-\\draw [in=90, out=-90] (20.center) to (21.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (11.center) to (12.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (14.center) to (15.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (17.center) to (18.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (20.center) to (21.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 0.5) {BX1};
+\\node [style=none] (10) at (1.25, -0.5) {BX1†};
+\\node [style=none, right] (13) at (1.35, 1.5) {s};
+\\node [style=none, right] (16) at (0.1, 0.15) {s};
+\\node [style=none, right] (19) at (2.6, 0.15) {s};
+\\node [style=none, right] (22) at (1.35, -0.85) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.25) {};
 \\node [] (2) at (3.0, 0.25) {};
 \\node [] (3) at (3.0, 0.75) {};
 \\node [] (4) at (-0.5, 0.75) {};
-\\node [style=none, fill=white] (5) at (1.25, 0.5) {BX1};
 \\node [] (6) at (1.25, 1.5) {};
 \\node [] (7) at (1.25, 0.75) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 1.5) {s};
 \\node [] (9) at (2.5, 0.25) {};
 \\node [] (10) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (11) at (2.6, 0.15) {s};
 \\node [] (12) at (5.0, 1.5) {};
 \\node [] (13) at (5.0, -0.25) {};
-\\node [style=none, fill=white, right] (14) at (5.1, 1.5) {s.r};
 \\node [] (15) at (0.0, 0.25) {};
 \\node [] (16) at (0.0, -1.5) {};
-\\node [style=none, fill=white, right] (17) at (0.1, 0.15) {s};
 \\node [] (18) at (3.75, -0.5) {};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=180, out=-90, looseness=0.4118] (10.center) to (18.center);
-\\draw [in=0, out=-90, looseness=0.4118] (13.center) to (18.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (15.center) to (16.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (10.center) to (18.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (13.center) to (18.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 0.5) {BX1};
+\\node [style=none, right] (8) at (1.35, 1.5) {s};
+\\node [style=none, right] (11) at (2.6, 0.15) {s};
+\\node [style=none, right] (14) at (5.1, 1.5) {s.r};
+\\node [style=none, right] (17) at (0.1, 0.15) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.75) {};
 \\node [] (2) at (3.0, 0.75) {};
 \\node [] (3) at (3.0, 1.25) {};
 \\node [] (4) at (-0.5, 1.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 1.0) {BX2};
 \\node [] (6) at (1.25, 2.0) {};
 \\node [] (7) at (1.25, 1.25) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 2.0) {s};
 \\node [] (9) at (2.5, 0.75) {};
 \\node [] (10) at (2.5, 0.25) {};
-\\node [style=none, fill=white, right] (11) at (2.6, 0.65) {s.r};
 \\node [] (12) at (5.0, 2.0) {};
 \\node [] (13) at (5.0, 0.25) {};
-\\node [style=none, fill=white, right] (14) at (5.1, 2.0) {s.r};
 \\node [] (15) at (7.5, 2.0) {};
 \\node [] (16) at (7.5, 0.25) {};
-\\node [style=none, fill=white, right] (17) at (7.6, 2.0) {s.r};
 \\node [] (18) at (0.0, 0.75) {};
 \\node [] (19) at (0.0, -0.75) {};
-\\node [style=none, fill=white, right] (20) at (0.1, 0.65) {s};
 \\node [] (21) at (3.75, -0.25) {};
 \\node [] (22) at (3.75, -0.75) {};
-\\node [style=none, fill=white, right] (23) at (3.85, -0.35) {s.r};
 \\node [] (24) at (6.25, -0.25) {};
 \\node [] (25) at (6.25, -2.0) {};
-\\node [style=none, fill=white, right] (26) at (6.35, -0.35) {s.r};
 \\node [circle, fill=black, scale=0.365] (27) at (5.0, 0.0) {};
 \\node [] (28) at (1.875, -1.0) {};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=90, out=-90] (18.center) to (19.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=180, looseness=0.4118] (27.center) to (21.center);
-\\draw [in=90, out=0, looseness=0.4118] (27.center) to (24.center);
-\\draw [in=180, out=-90, looseness=0.209] (10.center) to (27.center);
-\\draw [in=90, out=-90] (13.center) to (27.center);
-\\draw [in=0, out=-90, looseness=0.209] (16.center) to (27.center);
-\\draw [in=180, out=-90, looseness=0.2775] (19.center) to (28.center);
-\\draw [in=0, out=-90, looseness=0.2775] (22.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (15.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (18.center) to (19.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (24.center) to (25.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (27.center) to (21.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (27.center) to (24.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.209] (10.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (13.center) to (27.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.209] (16.center) to (27.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.2775] (19.center) to (28.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.2775] (22.center) to (28.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 1.0) {BX2};
+\\node [style=none, right] (8) at (1.35, 2.0) {s};
+\\node [style=none, right] (11) at (2.6, 0.65) {s.r};
+\\node [style=none, right] (14) at (5.1, 2.0) {s.r};
+\\node [style=none, right] (17) at (7.6, 2.0) {s.r};
+\\node [style=none, right] (20) at (0.1, 0.65) {s};
+\\node [style=none, right] (23) at (3.85, -0.35) {s.r};
+\\node [style=none, right] (26) at (6.35, -0.35) {s.r};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (0.75, 0.75) {};
 \\node [] (2) at (1.75, 0.75) {};
 \\node [] (3) at (2.0, 1.25) {};
 \\node [] (4) at (0.75, 1.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 1.0) {BX3};
 \\node [] (6) at (-0.5, -0.25) {};
 \\node [] (7) at (3.0, -0.25) {};
 \\node [] (8) at (3.25, 0.25) {};
 \\node [] (9) at (-0.5, 0.25) {};
-\\node [style=none, fill=white] (10) at (1.25, 0.0) {BX1};
 \\node [] (11) at (2.0, -1.25) {};
 \\node [] (12) at (3.25, -1.25) {};
 \\node [] (13) at (3.0, -0.75) {};
 \\node [] (14) at (2.0, -0.75) {};
-\\node [style=none, fill=white] (15) at (2.5, -1.0) {BX3†};
 \\node [] (16) at (-0.5, -1.25) {};
 \\node [] (17) at (0.75, -1.25) {};
 \\node [] (18) at (0.5, -0.75) {};
 \\node [] (19) at (-0.5, -0.75) {};
-\\node [style=none, fill=white] (20) at (0.0, -1.0) {BX3†};
 \\node [] (21) at (1.25, 0.75) {};
 \\node [] (22) at (1.25, 0.25) {};
-\\node [style=none, fill=white, right] (23) at (1.35, 0.65) {s};
 \\node [] (24) at (2.5, -0.25) {};
 \\node [] (25) at (2.5, -0.75) {};
-\\node [style=none, fill=white, right] (26) at (2.6, -0.35) {s};
 \\node [] (27) at (0.0, -0.25) {};
 \\node [] (28) at (0.0, -0.75) {};
-\\node [style=none, fill=white, right] (29) at (0.1, -0.35) {s};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [-, fill={white}] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
-\\draw [-, fill={white}] (11.center) to (12.center) to (13.center) to (14.center) to (11.center);
-\\draw [-, fill={white}] (16.center) to (17.center) to (18.center) to (19.center) to (16.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=-90] (27.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (6.center) to (7.center) to (8.center) to (9.center) to (6.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (11.center) to (12.center) to (13.center) to (14.center) to (11.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (16.center) to (17.center) to (18.center) to (19.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (24.center) to (25.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (27.center) to (28.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 1.0) {BX3};
+\\node [style=none] (10) at (1.25, 0.0) {BX1};
+\\node [style=none] (15) at (2.5, -1.0) {BX3†};
+\\node [style=none] (20) at (0.0, -1.0) {BX3†};
+\\node [style=none, right] (23) at (1.35, 0.65) {s};
+\\node [style=none, right] (26) at (2.6, -0.35) {s};
+\\node [style=none, right] (29) at (0.1, -0.35) {s};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
@@ -498,98 +533,105 @@ def test_tikz_drawing(diagram, tikz, capsys):
     assert tikz_op == tikz
 
 
-expected_equation_tikz = """\\begin{tikzpicture}[baseline=(0.base)]
+expected_equation_tikz = """% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.75) {};
 \\node [] (2) at (3.0, 0.75) {};
 \\node [] (3) at (3.25, 1.25) {};
 \\node [] (4) at (-0.5, 1.25) {};
-\\node [style=none, fill=white] (5) at (1.25, 1.0) {BX2};
 \\node [] (6) at (1.25, 2.0) {};
 \\node [] (7) at (1.25, 1.25) {};
-\\node [style=none, fill=white, right] (8) at (1.35, 2.0) {s};
 \\node [] (9) at (2.5, 0.75) {};
 \\node [] (10) at (2.5, 0.25) {};
-\\node [style=none, fill=white, right] (11) at (2.6, 0.65) {s.r};
 \\node [] (12) at (5.0, 2.0) {};
 \\node [] (13) at (5.0, 0.25) {};
-\\node [style=none, fill=white, right] (14) at (5.1, 2.0) {s.r};
 \\node [] (15) at (7.5, 2.0) {};
 \\node [] (16) at (7.5, 0.25) {};
-\\node [style=none, fill=white, right] (17) at (7.6, 2.0) {s.r};
 \\node [] (18) at (0.0, 0.75) {};
 \\node [] (19) at (0.0, -0.75) {};
-\\node [style=none, fill=white, right] (20) at (0.1, 0.65) {s};
 \\node [] (21) at (3.75, -0.25) {};
 \\node [] (22) at (3.75, -0.75) {};
-\\node [style=none, fill=white, right] (23) at (3.85, -0.35) {s.r};
 \\node [] (24) at (6.25, -0.25) {};
 \\node [] (25) at (6.25, -2.0) {};
-\\node [style=none, fill=white, right] (26) at (6.35, -0.35) {s.r};
 \\node [circle, fill=black, scale=0.365] (27) at (5.0, 0.0) {};
 \\node [] (28) at (1.875, -1.0) {};
-\\node [style=none, fill=white] (29) at (8.6, 0) {=};
 \\node [] (30) at (9.1, -1.25) {};
 \\node [] (31) at (12.85, -1.25) {};
 \\node [] (32) at (12.6, -0.75) {};
 \\node [] (33) at (9.1, -0.75) {};
-\\node [style=none, fill=white] (34) at (10.85, -1.0) {BX2†};
 \\node [] (35) at (13.35, 0.75) {};
 \\node [] (36) at (13.35, 0.25) {};
-\\node [style=none, fill=white, right] (37) at (13.45, 0.65) {s.r};
 \\node [] (38) at (15.85, 2.0) {};
 \\node [] (39) at (15.85, 0.25) {};
-\\node [style=none, fill=white, right] (40) at (15.95, 2.0) {s.r};
 \\node [] (41) at (9.6, 0.75) {};
 \\node [] (42) at (9.6, -0.75) {};
-\\node [style=none, fill=white, right] (43) at (9.7, 0.65) {s};
 \\node [] (44) at (12.1, -0.25) {};
 \\node [] (45) at (12.1, -0.75) {};
-\\node [style=none, fill=white, right] (46) at (12.2, -0.35) {s.r};
 \\node [] (47) at (10.85, -1.25) {};
 \\node [] (48) at (10.85, -2.0) {};
-\\node [style=none, fill=white, right] (49) at (10.95, -1.35) {s};
 \\node [] (50) at (14.6, -0.25) {};
 \\node [] (51) at (14.6, -2.0) {};
-\\node [style=none, fill=white, right] (52) at (14.7, -0.35) {s.r};
 \\node [] (53) at (17.1, -0.25) {};
 \\node [] (54) at (17.1, -2.0) {};
-\\node [style=none, fill=white, right] (55) at (17.200000000000003, -0.35) {s.r};
 \\node [] (56) at (12.1, 1.0) {};
 \\node [circle, fill=black, scale=0.242] (57) at (14.6, 0.0) {};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=90, out=-90] (12.center) to (13.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=90, out=-90] (18.center) to (19.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=180, looseness=0.4118] (27.center) to (21.center);
-\\draw [in=90, out=0, looseness=0.4118] (27.center) to (24.center);
-\\draw [in=180, out=-90, looseness=0.209] (10.center) to (27.center);
-\\draw [in=90, out=-90] (13.center) to (27.center);
-\\draw [in=0, out=-90, looseness=0.209] (16.center) to (27.center);
-\\draw [in=180, out=-90, looseness=0.2775] (19.center) to (28.center);
-\\draw [in=0, out=-90, looseness=0.2775] (22.center) to (28.center);
-\\draw [-, fill={white}] (30.center) to (31.center) to (32.center) to (33.center) to (30.center);
-\\draw [in=90, out=-90] (35.center) to (36.center);
-\\draw [in=90, out=-90] (38.center) to (39.center);
-\\draw [in=90, out=-90] (41.center) to (42.center);
-\\draw [in=90, out=-90] (44.center) to (45.center);
-\\draw [in=90, out=-90] (47.center) to (48.center);
-\\draw [in=90, out=-90] (50.center) to (51.center);
-\\draw [in=90, out=-90] (53.center) to (54.center);
-\\draw [in=90, out=180, looseness=0.209] (56.center) to (41.center);
-\\draw [in=90, out=0, looseness=0.4118] (56.center) to (35.center);
-\\draw [in=90, out=180, looseness=0.209] (57.center) to (44.center);
-\\draw [in=90, out=-90] (57.center) to (50.center);
-\\draw [in=90, out=0, looseness=0.209] (57.center) to (53.center);
-\\draw [in=180, out=-90, looseness=0.4118] (36.center) to (57.center);
-\\draw [in=0, out=-90, looseness=0.4118] (39.center) to (57.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (4.center) to (1.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (6.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (9.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (12.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (15.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (18.center) to (19.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (24.center) to (25.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (27.center) to (21.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (27.center) to (24.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.209] (10.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (13.center) to (27.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.209] (16.center) to (27.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.2775] (19.center) to (28.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.2775] (22.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (30.center) to (31.center) to (32.center) to (33.center) to (30.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (35.center) to (36.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (38.center) to (39.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (41.center) to (42.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (44.center) to (45.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (47.center) to (48.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (50.center) to (51.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (53.center) to (54.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.209] (56.center) to (41.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (56.center) to (35.center);
+\\draw [in=90, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.209] (57.center) to (44.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (57.center) to (50.center);
+\\draw [in=90, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.209] (57.center) to (53.center);
+\\draw [in=180, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (36.center) to (57.center);
+\\draw [in=0, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt, looseness=0.4118] (39.center) to (57.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (5) at (1.25, 1.0) {BX2};
+\\node [style=none, right] (8) at (1.35, 2.0) {s};
+\\node [style=none, right] (11) at (2.6, 0.65) {s.r};
+\\node [style=none, right] (14) at (5.1, 2.0) {s.r};
+\\node [style=none, right] (17) at (7.6, 2.0) {s.r};
+\\node [style=none, right] (20) at (0.1, 0.65) {s};
+\\node [style=none, right] (23) at (3.85, -0.35) {s.r};
+\\node [style=none, right] (26) at (6.35, -0.35) {s.r};
+\\node [style=none] (29) at (8.6, 0) {=};
+\\node [style=none] (34) at (10.85, -1.0) {BX2†};
+\\node [style=none, right] (37) at (13.45, 0.65) {s.r};
+\\node [style=none, right] (40) at (15.95, 2.0) {s.r};
+\\node [style=none, right] (43) at (9.7, 0.65) {s};
+\\node [style=none, right] (46) at (12.2, -0.35) {s.r};
+\\node [style=none, right] (49) at (10.95, -1.35) {s};
+\\node [style=none, right] (52) at (14.7, -0.35) {s.r};
+\\node [style=none, right] (55) at (17.200000000000003, -0.35) {s.r};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 

From 0f3c286cb18e9684f9bb4163658042b423e76e2b Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 21 Nov 2024 16:38:11 +0000
Subject: [PATCH 39/41] update circuit-drawing tests with linewidth changes

---
 tests/backend/test_circuit_drawing.py | 670 ++++++++++++++------------
 1 file changed, 356 insertions(+), 314 deletions(-)

diff --git a/tests/backend/test_circuit_drawing.py b/tests/backend/test_circuit_drawing.py
index 59c27778..c6720bef 100644
--- a/tests/backend/test_circuit_drawing.py
+++ b/tests/backend/test_circuit_drawing.py
@@ -34,26 +34,27 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 ]
 
 
-tikz_outputs = ["""\\begin{tikzpicture}[baseline=(0.base)]
+tikz_outputs = ["""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 1.75) {};
 \\node [] (2) at (0.5, 1.75) {};
 \\node [] (3) at (0.0, 2.25) {};
-\\node [style=none, fill=white] (4) at (0, 2.0) {0};
 \\node [] (5) at (2.0, 1.75) {};
 \\node [] (6) at (3.0, 1.75) {};
 \\node [] (7) at (2.5, 2.25) {};
-\\node [style=none, fill=white] (8) at (2.5, 2.0) {0};
 \\node [] (9) at (4.5, 1.75) {};
 \\node [] (10) at (5.5, 1.75) {};
 \\node [] (11) at (5.0, 2.25) {};
-\\node [style=none, fill=white] (12) at (5.0, 2.0) {0};
 \\node [] (13) at (-0.5, 0.75) {};
 \\node [] (14) at (0.5, 0.75) {};
 \\node [] (15) at (0.5, 1.25) {};
 \\node [] (16) at (-0.5, 1.25) {};
-\\node [style=none, fill=white] (17) at (0.0, 1.0) {H};
 \\node [circle, black] (18) at (0.0, 0.0) {};
 \\node [circle, white] (19) at (2.5, 0.0) {};
 \\node [plus] (20) at (2.5, 0.0) {};
@@ -70,45 +71,56 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (31) at (2.5, -0.75) {};
 \\node [] (32) at (0.0, 1.75) {};
 \\node [] (33) at (0.0, 1.25) {};
-\\node [style=none, fill=white, right] (34) at (0.1, 1.65) {qubit};
 \\node [] (35) at (0.0, 0.75) {};
-\\node [style=none, fill=white, right] (36) at (0.1, 0.65) {qubit};
 \\node [] (37) at (2.5, 1.75) {};
-\\node [style=none, fill=white, right] (38) at (2.6, 1.65) {qubit};
-\\node [style=none, fill=white, right] (39) at (2.6, -0.35) {qubit};
 \\node [] (40) at (5.0, 1.75) {};
-\\node [style=none, fill=white, right] (41) at (5.1, 1.65) {qubit};
 \\node [] (42) at (0.0, -2.0) {};
-\\node [style=none, fill=white, right] (43) at (0.1, -0.35) {qubit};
 \\node [] (44) at (2.5, -2.0) {};
-\\node [style=none, fill=white, right] (45) at (2.6, -1.35) {qubit};
 \\node [] (46) at (5.0, -2.0) {};
-\\node [style=none, fill=white, right] (47) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [-, fill={white}] (1.center) to (2.center) to (3.center) to (1.center);
-\\draw [-, fill={white}] (5.center) to (6.center) to (7.center) to (5.center);
-\\draw [-, fill={white}] (9.center) to (10.center) to (11.center) to (9.center);
-\\draw [-, fill={white}] (13.center) to (14.center) to (15.center) to (16.center) to (13.center);
-\\draw [in=90, out=-90] (21.center) to (22.center);
-\\draw [in=90, out=-90] (23.center) to (24.center);
-\\draw [in=180, out=0] (18.center) to (20.center);
-\\draw [in=90, out=-90] (28.center) to (29.center);
-\\draw [in=90, out=-90] (30.center) to (31.center);
-\\draw [in=180, out=0] (25.center) to (27.center);
-\\draw [in=90, out=-90] (32.center) to (33.center);
-\\draw [in=90, out=-90] (35.center) to (24.center);
-\\draw [in=90, out=-90] (37.center) to (22.center);
-\\draw [in=90, out=-90] (21.center) to (31.center);
-\\draw [in=90, out=-90] (40.center) to (29.center);
-\\draw [in=90, out=-90] (23.center) to (42.center);
-\\draw [in=90, out=-90] (30.center) to (44.center);
-\\draw [in=90, out=-90] (28.center) to (46.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (5.center) to (6.center) to (7.center) to (5.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (9.center) to (10.center) to (11.center) to (9.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (13.center) to (14.center) to (15.center) to (16.center) to (13.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (23.center) to (24.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (18.center) to (20.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (28.center) to (29.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (30.center) to (31.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (25.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (32.center) to (33.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (35.center) to (24.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (37.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (21.center) to (31.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (40.center) to (29.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (23.center) to (42.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (30.center) to (44.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (28.center) to (46.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (4) at (0, 2.0) {0};
+\\node [style=none] (8) at (2.5, 2.0) {0};
+\\node [style=none] (12) at (5.0, 2.0) {0};
+\\node [style=none] (17) at (0.0, 1.0) {H};
+\\node [style=none, right] (34) at (0.1, 1.65) {qubit};
+\\node [style=none, right] (36) at (0.1, 0.65) {qubit};
+\\node [style=none, right] (38) at (2.6, 1.65) {qubit};
+\\node [style=none, right] (39) at (2.6, -0.35) {qubit};
+\\node [style=none, right] (41) at (5.1, 1.65) {qubit};
+\\node [style=none, right] (43) at (0.1, -0.35) {qubit};
+\\node [style=none, right] (45) at (2.6, -1.35) {qubit};
+\\node [style=none, right] (47) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [circle, black] (1) at (2.5, 2.0) {};
@@ -122,15 +134,12 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (9) at (3.0, 0.75) {};
 \\node [] (10) at (3.0, 1.25) {};
 \\node [] (11) at (2.0, 1.25) {};
-\\node [style=none, fill=white] (12) at (2.5, 1.0) {H};
 \\node [] (13) at (2.0, 0.25) {};
 \\node [] (14) at (3.0, 0.25) {};
 \\node [] (15) at (2.5, -0.25) {};
-\\node [style=none, fill=white] (16) at (2.5, 0.0) {0};
 \\node [] (17) at (4.5, 1.25) {};
 \\node [] (18) at (5.5, 1.25) {};
 \\node [] (19) at (5.0, 0.75) {};
-\\node [style=none, fill=white] (20) at (5.0, 1.0) {0};
 \\node [circle, black] (21) at (0.0, -1.0) {};
 \\node [circle, white] (22) at (7.5, -1.0) {};
 \\node [plus] (23) at (7.5, -1.0) {};
@@ -142,66 +151,76 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (29) at (0.5, -2.25) {};
 \\node [] (30) at (0.5, -1.75) {};
 \\node [] (31) at (-0.5, -1.75) {};
-\\node [style=none, fill=white] (32) at (0.0, -2.0) {H};
 \\node [] (33) at (-0.5, -2.75) {};
 \\node [] (34) at (0.5, -2.75) {};
 \\node [] (35) at (0.0, -3.25) {};
-\\node [style=none, fill=white] (36) at (0.0, -3.0) {0};
 \\node [] (37) at (7.0, -1.75) {};
 \\node [] (38) at (8.0, -1.75) {};
 \\node [] (39) at (7.5, -2.25) {};
-\\node [style=none, fill=white] (40) at (7.5, -2.0) {0};
 \\node [] (41) at (2.5, 3.0) {};
-\\node [style=none, fill=white, right] (42) at (2.6, 3.0) {qubit};
 \\node [] (43) at (5.0, 3.0) {};
-\\node [style=none, fill=white, right] (44) at (5.1, 3.0) {qubit};
 \\node [] (45) at (2.5, 1.25) {};
-\\node [style=none, fill=white, right] (46) at (2.6, 1.65) {qubit};
 \\node [] (47) at (2.5, 0.75) {};
 \\node [] (48) at (2.5, 0.25) {};
-\\node [style=none, fill=white, right] (49) at (2.6, 0.65) {qubit};
 \\node [] (50) at (5.0, 1.25) {};
-\\node [style=none, fill=white, right] (51) at (5.1, 1.65) {qubit};
 \\node [] (52) at (0.0, 3.0) {};
-\\node [style=none, fill=white, right] (53) at (0.1, 3.0) {qubit};
 \\node [] (54) at (7.5, 3.0) {};
-\\node [style=none, fill=white, right] (55) at (7.6, 3.0) {qubit};
 \\node [] (56) at (0.0, -1.75) {};
-\\node [style=none, fill=white, right] (57) at (0.1, -1.35) {qubit};
 \\node [] (58) at (0.0, -2.25) {};
 \\node [] (59) at (0.0, -2.75) {};
-\\node [style=none, fill=white, right] (60) at (0.1, -2.35) {qubit};
 \\node [] (61) at (7.5, -1.75) {};
-\\node [style=none, fill=white, right] (62) at (7.6, -1.35) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [in=90, out=-90] (4.center) to (5.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=180, out=0] (1.center) to (3.center);
-\\draw [-, fill={white}] (8.center) to (9.center) to (10.center) to (11.center) to (8.center);
-\\draw [-, fill={white}] (13.center) to (14.center) to (15.center) to (13.center);
-\\draw [-, fill={white}] (17.center) to (18.center) to (19.center) to (17.center);
-\\draw [in=90, out=-90] (24.center) to (25.center);
-\\draw [in=90, out=-90] (26.center) to (27.center);
-\\draw [in=180, out=0] (21.center) to (23.center);
-\\draw [-, fill={white}] (28.center) to (29.center) to (30.center) to (31.center) to (28.center);
-\\draw [-, fill={white}] (33.center) to (34.center) to (35.center) to (33.center);
-\\draw [-, fill={white}] (37.center) to (38.center) to (39.center) to (37.center);
-\\draw [in=90, out=-90] (41.center) to (7.center);
-\\draw [in=90, out=-90] (43.center) to (5.center);
-\\draw [in=90, out=-90] (6.center) to (45.center);
-\\draw [in=90, out=-90] (47.center) to (48.center);
-\\draw [in=90, out=-90] (4.center) to (50.center);
-\\draw [in=90, out=-90] (52.center) to (27.center);
-\\draw [in=90, out=-90] (54.center) to (25.center);
-\\draw [in=90, out=-90] (26.center) to (56.center);
-\\draw [in=90, out=-90] (58.center) to (59.center);
-\\draw [in=90, out=-90] (24.center) to (61.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (4.center) to (5.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (6.center) to (7.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (1.center) to (3.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (8.center) to (9.center) to (10.center) to (11.center) to (8.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (13.center) to (14.center) to (15.center) to (13.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (17.center) to (18.center) to (19.center) to (17.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (24.center) to (25.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (26.center) to (27.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (21.center) to (23.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (28.center) to (29.center) to (30.center) to (31.center) to (28.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (33.center) to (34.center) to (35.center) to (33.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (37.center) to (38.center) to (39.center) to (37.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (41.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (43.center) to (5.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (6.center) to (45.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (47.center) to (48.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (4.center) to (50.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (52.center) to (27.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (54.center) to (25.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (26.center) to (56.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (58.center) to (59.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (24.center) to (61.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (12) at (2.5, 1.0) {H};
+\\node [style=none] (16) at (2.5, 0.0) {0};
+\\node [style=none] (20) at (5.0, 1.0) {0};
+\\node [style=none] (32) at (0.0, -2.0) {H};
+\\node [style=none] (36) at (0.0, -3.0) {0};
+\\node [style=none] (40) at (7.5, -2.0) {0};
+\\node [style=none, right] (42) at (2.6, 3.0) {qubit};
+\\node [style=none, right] (44) at (5.1, 3.0) {qubit};
+\\node [style=none, right] (46) at (2.6, 1.65) {qubit};
+\\node [style=none, right] (49) at (2.6, 0.65) {qubit};
+\\node [style=none, right] (51) at (5.1, 1.65) {qubit};
+\\node [style=none, right] (53) at (0.1, 3.0) {qubit};
+\\node [style=none, right] (55) at (7.6, 3.0) {qubit};
+\\node [style=none, right] (57) at (0.1, -1.35) {qubit};
+\\node [style=none, right] (60) at (0.1, -2.35) {qubit};
+\\node [style=none, right] (62) at (7.6, -1.35) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [circle, black] (1) at (0.0, 5.5) {};
@@ -255,7 +274,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (49) at (3.0, -0.75) {};
 \\node [] (50) at (3.0, -0.25) {};
 \\node [] (51) at (2.0, -0.25) {};
-\\node [style=none, fill=white] (52) at (2.5, -0.5) {U};
 \\node [] (53) at (5.0, -0.75) {};
 \\node [] (54) at (5.0, -0.25) {};
 \\node [] (55) at (3.0, -0.5) {};
@@ -264,7 +282,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (58) at (3.0, -1.75) {};
 \\node [] (59) at (3.0, -1.25) {};
 \\node [] (60) at (2.0, -1.25) {};
-\\node [style=none, fill=white] (61) at (2.5, -1.5) {U};
 \\node [] (62) at (0.0, -1.75) {};
 \\node [] (63) at (0.0, -1.25) {};
 \\node [] (64) at (2.0, -1.5) {};
@@ -273,7 +290,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (67) at (0.5, -2.75) {};
 \\node [] (68) at (0.5, -2.25) {};
 \\node [] (69) at (-0.5, -2.25) {};
-\\node [style=none, fill=white] (70) at (0.0, -2.5) {U};
 \\node [] (71) at (5.0, -2.75) {};
 \\node [] (72) at (5.0, -2.25) {};
 \\node [] (73) at (2.5, -2.75) {};
@@ -284,7 +300,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (78) at (5.5, -3.75) {};
 \\node [] (79) at (5.5, -3.25) {};
 \\node [] (80) at (4.5, -3.25) {};
-\\node [style=none, fill=white] (81) at (5.0, -3.5) {U};
 \\node [] (82) at (0.0, -3.75) {};
 \\node [] (83) at (0.0, -3.25) {};
 \\node [] (84) at (2.5, -3.75) {};
@@ -295,7 +310,6 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (89) at (3.0, -4.75) {};
 \\node [] (90) at (3.0, -4.25) {};
 \\node [] (91) at (-0.5, -4.25) {};
-\\node [style=none, fill=white] (92) at (2.0, -4.5) {U2};
 \\node [] (93) at (5.0, -4.75) {};
 \\node [] (94) at (5.0, -4.25) {};
 \\node [] (95) at (3.0, -4.5) {};
@@ -304,145 +318,157 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (98) at (5.5, -5.75) {};
 \\node [] (99) at (5.5, -5.25) {};
 \\node [] (100) at (2.0, -5.25) {};
-\\node [style=none, fill=white] (101) at (3.0, -5.5) {U2};
 \\node [] (102) at (0.0, -5.75) {};
 \\node [] (103) at (0.0, -5.25) {};
 \\node [] (104) at (2.0, -5.5) {};
 \\node [] (105) at (0.0, 6.5) {};
-\\node [style=none, fill=white, right] (106) at (0.1, 6.5) {qubit};
 \\node [] (107) at (2.5, 6.5) {};
-\\node [style=none, fill=white, right] (108) at (2.6, 6.5) {qubit};
-\\node [style=none, fill=white, right] (109) at (0.1, 5.15) {qubit};
-\\node [style=none, fill=white, right] (110) at (2.6, 5.15) {qubit};
-\\node [style=none, fill=white, right] (111) at (2.6, 4.15) {qubit};
 \\node [] (112) at (5.0, 6.5) {};
-\\node [style=none, fill=white, right] (113) at (5.1, 6.5) {qubit};
-\\node [style=none, fill=white, right] (114) at (2.6, 3.15) {qubit};
-\\node [style=none, fill=white, right] (115) at (5.1, 3.15) {qubit};
-\\node [style=none, fill=white, right] (116) at (0.1, 4.15) {qubit};
-\\node [style=none, fill=white, right] (117) at (2.6, 2.15) {qubit};
-\\node [style=none, fill=white, right] (118) at (5.1, 2.15) {qubit};
-\\node [style=none, fill=white, right] (119) at (0.1, 1.15) {qubit};
-\\node [style=none, fill=white, right] (120) at (2.6, 1.15) {qubit};
-\\node [style=none, fill=white, right] (121) at (5.1, 1.15) {qubit};
 \\node [] (122) at (2.5, -0.25) {};
-\\node [style=none, fill=white, right] (123) at (2.6, 0.15) {qubit};
-\\node [style=none, fill=white, right] (124) at (5.1, 0.15) {qubit};
-\\node [style=none, fill=white, right] (125) at (0.1, 0.15) {qubit};
 \\node [] (126) at (2.5, -0.75) {};
 \\node [] (127) at (2.5, -1.25) {};
-\\node [style=none, fill=white, right] (128) at (2.6, -0.85) {qubit};
 \\node [] (129) at (0.0, -2.25) {};
-\\node [style=none, fill=white, right] (130) at (0.1, -1.85) {qubit};
 \\node [] (131) at (2.5, -1.75) {};
-\\node [style=none, fill=white, right] (132) at (2.6, -1.85) {qubit};
-\\node [style=none, fill=white, right] (133) at (5.1, -0.85) {qubit};
 \\node [] (134) at (0.0, -2.75) {};
-\\node [style=none, fill=white, right] (135) at (0.1, -2.85) {qubit};
-\\node [style=none, fill=white, right] (136) at (2.6, -2.85) {qubit};
 \\node [] (137) at (5.0, -3.25) {};
-\\node [style=none, fill=white, right] (138) at (5.1, -2.85) {qubit};
 \\node [] (139) at (0.0, -4.25) {};
-\\node [style=none, fill=white, right] (140) at (0.1, -3.85) {qubit};
 \\node [] (141) at (2.5, -4.25) {};
-\\node [style=none, fill=white, right] (142) at (2.6, -3.85) {qubit};
 \\node [] (143) at (5.0, -3.75) {};
-\\node [style=none, fill=white, right] (144) at (5.1, -3.85) {qubit};
 \\node [] (145) at (0.0, -4.75) {};
-\\node [style=none, fill=white, right] (146) at (0.1, -4.85) {qubit};
 \\node [] (147) at (2.5, -4.75) {};
 \\node [] (148) at (2.5, -5.25) {};
-\\node [style=none, fill=white, right] (149) at (2.6, -4.85) {qubit};
 \\node [] (150) at (5.0, -5.25) {};
-\\node [style=none, fill=white, right] (151) at (5.1, -4.85) {qubit};
 \\node [] (152) at (0.0, -6.5) {};
-\\node [style=none, fill=white, right] (153) at (0.1, -5.85) {qubit};
 \\node [] (154) at (2.5, -5.75) {};
 \\node [] (155) at (2.5, -6.5) {};
-\\node [style=none, fill=white, right] (156) at (2.6, -5.85) {qubit};
 \\node [] (157) at (5.0, -5.75) {};
 \\node [] (158) at (5.0, -6.5) {};
-\\node [style=none, fill=white, right] (159) at (5.1, -5.85) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [in=90, out=-90] (4.center) to (5.center);
-\\draw [in=90, out=-90] (6.center) to (7.center);
-\\draw [in=180, out=0] (1.center) to (3.center);
-\\draw [in=90, out=-90] (11.center) to (12.center);
-\\draw [in=90, out=-90] (13.center) to (14.center);
-\\draw [in=0, out=180] (8.center) to (10.center);
-\\draw [in=90, out=-90] (18.center) to (19.center);
-\\draw [in=90, out=-90] (20.center) to (21.center);
-\\draw [in=180, out=0] (15.center) to (17.center);
-\\draw [in=90, out=-90] (25.center) to (26.center);
-\\draw [in=90, out=-90] (27.center) to (28.center);
-\\draw [in=0, out=180] (22.center) to (24.center);
-\\draw [in=90, out=-90] (32.center) to (33.center);
-\\draw [in=90, out=-90] (34.center) to (35.center);
-\\draw [in=90, out=-90] (36.center) to (37.center);
-\\draw [in=180, out=0] (29.center) to (31.center);
-\\draw [in=90, out=-90] (41.center) to (42.center);
-\\draw [in=90, out=-90] (43.center) to (44.center);
-\\draw [in=90, out=-90] (45.center) to (46.center);
-\\draw [in=0, out=180] (38.center) to (40.center);
-\\draw [-, fill={white}] (48.center) to (49.center) to (50.center) to (51.center) to (48.center);
-\\draw [in=90, out=-90] (53.center) to (54.center);
-\\draw [in=0, out=180] (47.center) to (55.center);
-\\draw [-, fill={white}] (57.center) to (58.center) to (59.center) to (60.center) to (57.center);
-\\draw [in=90, out=-90] (62.center) to (63.center);
-\\draw [in=180, out=0] (56.center) to (64.center);
-\\draw [-, fill={white}] (66.center) to (67.center) to (68.center) to (69.center) to (66.center);
-\\draw [in=90, out=-90] (71.center) to (72.center);
-\\draw [in=90, out=-90] (73.center) to (74.center);
-\\draw [in=0, out=180] (65.center) to (75.center);
-\\draw [-, fill={white}] (77.center) to (78.center) to (79.center) to (80.center) to (77.center);
-\\draw [in=90, out=-90] (82.center) to (83.center);
-\\draw [in=90, out=-90] (84.center) to (85.center);
-\\draw [in=180, out=0] (76.center) to (86.center);
-\\draw [-, fill={white}] (88.center) to (89.center) to (90.center) to (91.center) to (88.center);
-\\draw [in=90, out=-90] (93.center) to (94.center);
-\\draw [in=0, out=180] (87.center) to (95.center);
-\\draw [-, fill={white}] (97.center) to (98.center) to (99.center) to (100.center) to (97.center);
-\\draw [in=90, out=-90] (102.center) to (103.center);
-\\draw [in=180, out=0] (96.center) to (104.center);
-\\draw [in=90, out=-90] (105.center) to (7.center);
-\\draw [in=90, out=-90] (107.center) to (5.center);
-\\draw [in=90, out=-90] (6.center) to (12.center);
-\\draw [in=90, out=-90] (4.center) to (14.center);
-\\draw [in=90, out=-90] (13.center) to (21.center);
-\\draw [in=90, out=-90] (112.center) to (19.center);
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-\\draw [in=90, out=-90] (154.center) to (155.center);
-\\draw [in=90, out=-90] (157.center) to (158.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (4.center) to (5.center);
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+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (36.center) to (37.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (29.center) to (31.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (41.center) to (42.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (43.center) to (44.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (45.center) to (46.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (38.center) to (40.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (48.center) to (49.center) to (50.center) to (51.center) to (48.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (53.center) to (54.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (47.center) to (55.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (57.center) to (58.center) to (59.center) to (60.center) to (57.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (62.center) to (63.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (56.center) to (64.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (66.center) to (67.center) to (68.center) to (69.center) to (66.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (71.center) to (72.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (73.center) to (74.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (65.center) to (75.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (77.center) to (78.center) to (79.center) to (80.center) to (77.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (82.center) to (83.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (84.center) to (85.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (76.center) to (86.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (88.center) to (89.center) to (90.center) to (91.center) to (88.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (93.center) to (94.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (87.center) to (95.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (97.center) to (98.center) to (99.center) to (100.center) to (97.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (102.center) to (103.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (96.center) to (104.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (105.center) to (7.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (107.center) to (5.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (6.center) to (12.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (4.center) to (14.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (13.center) to (21.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (112.center) to (19.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (20.center) to (26.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (18.center) to (28.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (11.center) to (35.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (25.center) to (37.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (27.center) to (33.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (34.center) to (42.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (36.center) to (46.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (32.center) to (44.center);
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+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (41.center) to (63.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (126.center) to (127.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (62.center) to (129.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (131.center) to (74.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (53.center) to (72.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (134.center) to (83.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (73.center) to (85.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (71.center) to (137.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (82.center) to (139.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (84.center) to (141.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (143.center) to (94.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (145.center) to (103.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (147.center) to (148.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (93.center) to (150.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (102.center) to (152.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (154.center) to (155.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (157.center) to (158.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (52) at (2.5, -0.5) {U};
+\\node [style=none] (61) at (2.5, -1.5) {U};
+\\node [style=none] (70) at (0.0, -2.5) {U};
+\\node [style=none] (81) at (5.0, -3.5) {U};
+\\node [style=none] (92) at (2.0, -4.5) {U2};
+\\node [style=none] (101) at (3.0, -5.5) {U2};
+\\node [style=none, right] (106) at (0.1, 6.5) {qubit};
+\\node [style=none, right] (108) at (2.6, 6.5) {qubit};
+\\node [style=none, right] (109) at (0.1, 5.15) {qubit};
+\\node [style=none, right] (110) at (2.6, 5.15) {qubit};
+\\node [style=none, right] (111) at (2.6, 4.15) {qubit};
+\\node [style=none, right] (113) at (5.1, 6.5) {qubit};
+\\node [style=none, right] (114) at (2.6, 3.15) {qubit};
+\\node [style=none, right] (115) at (5.1, 3.15) {qubit};
+\\node [style=none, right] (116) at (0.1, 4.15) {qubit};
+\\node [style=none, right] (117) at (2.6, 2.15) {qubit};
+\\node [style=none, right] (118) at (5.1, 2.15) {qubit};
+\\node [style=none, right] (119) at (0.1, 1.15) {qubit};
+\\node [style=none, right] (120) at (2.6, 1.15) {qubit};
+\\node [style=none, right] (121) at (5.1, 1.15) {qubit};
+\\node [style=none, right] (123) at (2.6, 0.15) {qubit};
+\\node [style=none, right] (124) at (5.1, 0.15) {qubit};
+\\node [style=none, right] (125) at (0.1, 0.15) {qubit};
+\\node [style=none, right] (128) at (2.6, -0.85) {qubit};
+\\node [style=none, right] (130) at (0.1, -1.85) {qubit};
+\\node [style=none, right] (132) at (2.6, -1.85) {qubit};
+\\node [style=none, right] (133) at (5.1, -0.85) {qubit};
+\\node [style=none, right] (135) at (0.1, -2.85) {qubit};
+\\node [style=none, right] (136) at (2.6, -2.85) {qubit};
+\\node [style=none, right] (138) at (5.1, -2.85) {qubit};
+\\node [style=none, right] (140) at (0.1, -3.85) {qubit};
+\\node [style=none, right] (142) at (2.6, -3.85) {qubit};
+\\node [style=none, right] (144) at (5.1, -3.85) {qubit};
+\\node [style=none, right] (146) at (0.1, -4.85) {qubit};
+\\node [style=none, right] (149) at (2.6, -4.85) {qubit};
+\\node [style=none, right] (151) at (5.1, -4.85) {qubit};
+\\node [style=none, right] (153) at (0.1, -5.85) {qubit};
+\\node [style=none, right] (156) at (2.6, -5.85) {qubit};
+\\node [style=none, right] (159) at (5.1, -5.85) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [circle, black] (1) at (0.0, 1.0) {};
@@ -476,77 +502,80 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (29) at (5.0, -1.25) {};
 \\node [] (30) at (5.0, -0.75) {};
 \\node [] (31) at (0.0, 2.0) {};
-\\node [style=none, fill=white, right] (32) at (0.1, 2.0) {qubit};
 \\node [] (33) at (2.5, 2.0) {};
-\\node [style=none, fill=white, right] (34) at (2.6, 2.0) {qubit};
 \\node [] (35) at (5.0, 2.0) {};
-\\node [style=none, fill=white, right] (36) at (5.1, 2.0) {qubit};
-\\node [style=none, fill=white, right] (37) at (0.1, 0.65) {qubit};
-\\node [style=none, fill=white, right] (38) at (2.6, 0.65) {qubit};
-\\node [style=none, fill=white, right] (39) at (5.1, 0.65) {qubit};
-\\node [style=none, fill=white, right] (40) at (0.1, -0.35) {qubit};
-\\node [style=none, fill=white, right] (41) at (2.6, -0.35) {qubit};
-\\node [style=none, fill=white, right] (42) at (5.1, -0.35) {qubit};
 \\node [] (43) at (0.0, -2.0) {};
-\\node [style=none, fill=white, right] (44) at (0.1, -1.35) {qubit};
 \\node [] (45) at (2.5, -2.0) {};
-\\node [style=none, fill=white, right] (46) at (2.6, -1.35) {qubit};
 \\node [] (47) at (5.0, -2.0) {};
-\\node [style=none, fill=white, right] (48) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\begin{pgfonlayer}{edgelayer}
-\\draw [in=90, out=-90] (5.center) to (6.center);
-\\draw [in=90, out=-90] (7.center) to (8.center);
-\\draw [in=180, out=0] (2.center) to (4.center);
-\\draw [in=90, out=-90] (9.center) to (10.center);
-\\draw [in=180, out=0] (1.center) to (2.center);
-\\draw [in=90, out=-90] (15.center) to (16.center);
-\\draw [in=90, out=-90] (17.center) to (18.center);
-\\draw [in=0, out=180] (12.center) to (14.center);
-\\draw [in=90, out=-90] (19.center) to (20.center);
-\\draw [in=180, out=0] (11.center) to (14.center);
-\\draw [in=90, out=-90] (25.center) to (26.center);
-\\draw [in=90, out=-90] (27.center) to (28.center);
-\\draw [in=0, out=180] (22.center) to (24.center);
-\\draw [in=90, out=-90] (29.center) to (30.center);
-\\draw [in=0, out=180] (21.center) to (22.center);
-\\draw [in=90, out=-90] (31.center) to (10.center);
-\\draw [in=90, out=-90] (33.center) to (8.center);
-\\draw [in=90, out=-90] (35.center) to (6.center);
-\\draw [in=90, out=-90] (9.center) to (20.center);
-\\draw [in=90, out=-90] (7.center) to (16.center);
-\\draw [in=90, out=-90] (5.center) to (18.center);
-\\draw [in=90, out=-90] (19.center) to (26.center);
-\\draw [in=90, out=-90] (15.center) to (28.center);
-\\draw [in=90, out=-90] (17.center) to (30.center);
-\\draw [in=90, out=-90] (25.center) to (43.center);
-\\draw [in=90, out=-90] (27.center) to (45.center);
-\\draw [in=90, out=-90] (29.center) to (47.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (5.center) to (6.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (7.center) to (8.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (2.center) to (4.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (9.center) to (10.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (1.center) to (2.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (15.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (17.center) to (18.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (12.center) to (14.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (19.center) to (20.center);
+\\draw [in=180, out=0, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (11.center) to (14.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (25.center) to (26.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (27.center) to (28.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (22.center) to (24.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (29.center) to (30.center);
+\\draw [in=0, out=180, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (21.center) to (22.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (31.center) to (10.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (33.center) to (8.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (35.center) to (6.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (9.center) to (20.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (7.center) to (16.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (5.center) to (18.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (19.center) to (26.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (15.center) to (28.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (17.center) to (30.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (25.center) to (43.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (27.center) to (45.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (29.center) to (47.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none, right] (32) at (0.1, 2.0) {qubit};
+\\node [style=none, right] (34) at (2.6, 2.0) {qubit};
+\\node [style=none, right] (36) at (5.1, 2.0) {qubit};
+\\node [style=none, right] (37) at (0.1, 0.65) {qubit};
+\\node [style=none, right] (38) at (2.6, 0.65) {qubit};
+\\node [style=none, right] (39) at (5.1, 0.65) {qubit};
+\\node [style=none, right] (40) at (0.1, -0.35) {qubit};
+\\node [style=none, right] (41) at (2.6, -0.35) {qubit};
+\\node [style=none, right] (42) at (5.1, -0.35) {qubit};
+\\node [style=none, right] (44) at (0.1, -1.35) {qubit};
+\\node [style=none, right] (46) at (2.6, -1.35) {qubit};
+\\node [style=none, right] (48) at (5.1, -1.35) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
 """,
-"""\\begin{tikzpicture}[baseline=(0.base)]
+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
+\\begin{tikzpicture}[baseline=(0.base)]
 \\begin{pgfonlayer}{nodelayer}
 \\node (0) at (0, 0) {};
 \\node [] (1) at (-0.5, 0.25) {};
 \\node [] (2) at (0.5, 0.25) {};
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 \\node [] (17) at (7.0, 0.75) {};
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 \\node [] (20) at (0.5, -0.75) {};
 \\node [] (21) at (0.5, -0.25) {};
@@ -559,11 +588,9 @@ def __init__(self, name: str, dom, cod, data=None, is_mixed=True, **params):
 \\node [] (28) at (2.0, -0.25) {};
 \\node [] (29) at (3.0, -0.25) {};
 \\node [] (30) at (2.5, -0.75) {};
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 \\node [] (37) at (8.0, -0.25) {};
 \\node [] (38) at (7.1, -0.35) {};
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+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (14.center) to (15.center) to (16.center) to (17.center) to (14.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (19.center) to (20.center) to (21.center) to (22.center) to (19.center);
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+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (57.center) to (58.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (4) at (0.0, 0.5) {0};
+\\node [style=none] (8) at (2.5, 0.5) {1};
+\\node [style=none] (13) at (5.0, 0.5) {MixedState};
+\\node [style=none] (18) at (7.5, 0.5) {Encode};
+\\node [style=none] (31) at (2.5, -0.5) {0};
+\\node [style=none] (35) at (5.0, -0.5) {1};
+\\node [style=none, right] (44) at (7.6, 1.5) {bit};
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+\\node [style=none, right] (53) at (5.1, 0.15) {qubit};
+\\node [style=none, right] (56) at (7.6, 0.15) {qubit};
+\\node [style=none, right] (59) at (0.1, -0.85) {bit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 
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+"""% When embedding into a *.tex file, uncomment and include the following lines:
+% \\pgfdeclarelayer{nodelayer}
+% \\pgfdeclarelayer{edgelayer}
+% \\pgfdeclarelayer{labellayer}
+% \\pgfsetlayers{nodelayer, edgelayer, labellayer}
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+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (1.center) to (2.center) to (3.center) to (1.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (5.center) to (6.center) to (7.center) to (5.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (9.center) to (10.center) to (11.center) to (9.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (13.center) to (14.center) to (15.center) to (13.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (17.center) to (18.center) to (19.center) to (20.center) to (17.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (22.center) to (23.center) to (24.center) to (25.center) to (22.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (27.center) to (28.center) to (29.center) to (30.center) to (27.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (32.center) to (33.center) to (34.center) to (35.center) to (32.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (37.center) to (38.center) to (39.center) to (40.center) to (37.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (42.center) to (43.center) to (44.center) to (45.center) to (42.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (47.center) to (48.center) to (49.center) to (50.center) to (47.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (52.center) to (53.center) to (54.center) to (55.center) to (52.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (57.center) to (58.center) to (59.center) to (60.center) to (57.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (62.center) to (63.center) to (64.center) to (62.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (66.center) to (67.center) to (68.center) to (66.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (70.center) to (71.center) to (72.center) to (70.center);
+\\draw [-, fill={rgb,255: red,255; green,255; blue,255}, line width=0.4pt] (74.center) to (75.center) to (76.center) to (74.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (78.center) to (79.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (81.center) to (82.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (84.center) to (85.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (87.center) to (88.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (90.center) to (91.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (93.center) to (94.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (96.center) to (97.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (99.center) to (100.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (102.center) to (103.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (105.center) to (106.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (108.center) to (109.center);
+\\draw [in=90, out=-90, -, draw={rgb,255: red,0; green,0; blue,0}, line width=0.5pt] (111.center) to (112.center);
+\\end{pgfonlayer}
+\\begin{pgfonlayer}{labellayer}
+\\node [style=none] (4) at (0.0, 1.5) {0};
+\\node [style=none] (8) at (2.5, 1.5) {0};
+\\node [style=none] (12) at (7.5, 1.5) {0};
+\\node [style=none] (16) at (10.0, 1.5) {0};
+\\node [style=none] (21) at (0.0, 0.5) {S};
+\\node [style=none] (26) at (2.5, 0.5) {X};
+\\node [style=none] (31) at (7.5, 0.5) {Y};
+\\node [style=none] (36) at (10.0, 0.5) {Z};
+\\node [style=none] (41) at (0.0, -0.5) {Rx(0.3)};
+\\node [style=none] (46) at (2.5, -0.5) {Ry(0.2)};
+\\node [style=none] (51) at (5.0, 1.5) {0.500};
+\\node [style=none] (56) at (7.5, -0.5) {Rz(0.1)};
+\\node [style=none] (61) at (10.0, -0.5) {H};
+\\node [style=none] (65) at (0.0, -1.5) {0};
+\\node [style=none] (69) at (2.5, -1.5) {0};
+\\node [style=none] (73) at (7.5, -1.5) {0};
+\\node [style=none] (77) at (10.0, -1.5) {0};
+\\node [style=none, right] (80) at (0.1, 1.15) {qubit};
+\\node [style=none, right] (83) at (2.6, 1.15) {qubit};
+\\node [style=none, right] (86) at (7.6, 1.15) {qubit};
+\\node [style=none, right] (89) at (10.1, 1.15) {qubit};
+\\node [style=none, right] (92) at (0.1, 0.15) {qubit};
+\\node [style=none, right] (95) at (2.6, 0.15) {qubit};
+\\node [style=none, right] (98) at (7.6, 0.15) {qubit};
+\\node [style=none, right] (101) at (10.1, 0.15) {qubit};
+\\node [style=none, right] (104) at (0.1, -0.85) {qubit};
+\\node [style=none, right] (107) at (2.6, -0.85) {qubit};
+\\node [style=none, right] (110) at (7.6, -0.85) {qubit};
+\\node [style=none, right] (113) at (10.1, -0.85) {qubit};
 \\end{pgfonlayer}
 \\end{tikzpicture}
 

From 448107b2ef9a87c6171e104f17aeb0ab9bb022ec Mon Sep 17 00:00:00 2001
From: ragunathc <ragunath.chandrasekharan@quantinuum.com>
Date: Thu, 21 Nov 2024 16:45:31 +0000
Subject: [PATCH 40/41] make mypy happy

---
 lambeq/backend/drawing/drawing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index 12075273..a121024d 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -48,8 +48,8 @@
 )
 from lambeq.backend.drawing.text_printer import PregroupTextPrinter
 from lambeq.backend.drawing.tikz_backend import (
-    TikzBackend, WIRE_LINEWIDTH as TIKZ_WIRE_LINEWIDTH,
-    BOX_LINEWIDTH as TIKZ_BOX_LINEWIDTH
+    BOX_LINEWIDTH as TIKZ_BOX_LINEWIDTH, TikzBackend,
+    WIRE_LINEWIDTH as TIKZ_WIRE_LINEWIDTH
 )
 from lambeq.backend.grammar import Box, Diagram
 

From 9f3a9a84418396c1ead880943c27c77a3e544bf3 Mon Sep 17 00:00:00 2001
From: "Neil John D. Ortega" <neil.ortega@quantinuum.com>
Date: Fri, 22 Nov 2024 11:01:34 +0000
Subject: [PATCH 41/41] Add `box_linewidth` as param for `MatBackend` * Tidy up
 call

---
 lambeq/backend/drawing/drawable.py     | 51 +++++++++++++-------------
 lambeq/backend/drawing/drawing.py      |  8 ++--
 lambeq/backend/drawing/tikz_backend.py | 10 ++---
 3 files changed, 35 insertions(+), 34 deletions(-)

diff --git a/lambeq/backend/drawing/drawable.py b/lambeq/backend/drawing/drawable.py
index 513bab48..ad792a1b 100644
--- a/lambeq/backend/drawing/drawable.py
+++ b/lambeq/backend/drawing/drawable.py
@@ -95,7 +95,7 @@ class WireEndpoint:
 
     x: float
     y: float
-    noun_id: int = 0  # New attribute for wire noun
+    noun_id: int = 0    # New attribute for wire noun
     parent: Optional['BoxNode'] = None
 
     @property
@@ -778,8 +778,7 @@ class DrawableDiagramWithFrames(DrawableDiagram):
     frame, carrying all information necessary to render it.
 
     """
-    # add counter for Nouns
-    noun_id_counter = 1
+    noun_id_counter: int = 1
 
     def _make_space(self,
                     scan: list[int],
@@ -844,13 +843,13 @@ def _make_space(self,
         return x, y
 
     def _add_box_with_nouns(
-            self,
-            scan: list[int],
-            box: grammar.Box,
-            off: int,
-            x_pos: float,
-            y_pos: float,
-            input_nouns: list[int]
+        self,
+        scan: list[int],
+        box: grammar.Box,
+        off: int,
+        x_pos: float,
+        y_pos: float,
+        input_nouns: list[int]
     ) -> tuple[list[int], int, list[int]]:
         """Add a box to the graph, creating necessary wire endpoints.
 
@@ -862,6 +861,7 @@ def _add_box_with_nouns(
             The index of the newly added `BoxNode`
         input_nouns : list[int]
             The new order of input_nouns after adding the box
+
         """
         node = BoxNode(box, x_pos, y_pos)
 
@@ -880,10 +880,10 @@ def _add_box_with_nouns(
         for i, obj in enumerate(box.dom):
             idx = off + i
             nbr_idx = scan[off + i]
-            noun_id = (input_nouns[idx] if input_nouns
-                       and idx < len(input_nouns)
-                       else self.get_noun_id()
-                       )  # generate new noun id if needed
+            noun_id = (
+                input_nouns[idx] if (input_nouns and idx < len(input_nouns))
+                else self.get_noun_id()
+            )   # generate new noun_id if needed
 
             wire_end = WireEndpoint(WireEndpointType.DOM,
                                     obj=obj,
@@ -896,32 +896,31 @@ def _add_box_with_nouns(
             self._add_wire(nbr_idx, wire_idx)
 
         scan_insert = []
-        # if Swap, exchange the noun_ids
         if isinstance(box, grammar.Swap):
+            # If Swap, exchange the noun_ids
             if input_nouns and len(box.dom) > 1:
                 dom_idx_1 = off
                 dom_idx_2 = off + 1
                 input_nouns[dom_idx_1], input_nouns[dom_idx_2] = (
-                    input_nouns[dom_idx_2], input_nouns[dom_idx_1])
-        # if Spider, expand or shrink the noun_ids based on type
+                    input_nouns[dom_idx_2], input_nouns[dom_idx_1]
+                )
         elif isinstance(node.obj, grammar.Spider):
+            # If Spider, expand or shrink the noun_ids based on type
             if len(box.dom) == 1 and len(box.cod) > 1:
                 dom_noun = (input_nouns[off] if input_nouns
                             and off < len(input_nouns)
-                            else self.get_noun_id()
-                            )
+                            else self.get_noun_id())
                 expanded_colors = [dom_noun] * len(box.cod)
                 input_nouns = (input_nouns[:off] + expanded_colors
                                + input_nouns[off + len(box.dom):])
             elif len(box.dom) > 1 and len(box.cod) == 1:
                 cod_noun = (input_nouns[off] if input_nouns
                             and off < len(input_nouns)
-                            else self.get_noun_id()
-                            )
+                            else self.get_noun_id())
                 input_nouns = (input_nouns[:off] + [cod_noun]
                                + input_nouns[off + len(box.dom):])
 
-        num_output = off+len(box.cod)
+        num_output = off + len(box.cod)
         for i in range(num_output):
             if i < len(input_nouns):
                 pass
@@ -929,9 +928,9 @@ def _add_box_with_nouns(
                 # If we run out of input nouns, generate new ones
                 new_color = self.get_noun_id()
                 input_nouns.append(new_color)
+
         # Create a node representing each element in the box's codomain
         for i, obj in enumerate(box.cod):
-
             # If the box is a quantum gate, retain x coordinate of wires
             if box.category == quantum and len(box.dom) == len(box.cod):
                 nbr_idx = scan[off + i]
@@ -1066,14 +1065,14 @@ def calculate_bounds(self) -> tuple[float, float, float, float]:
         return min(all_xs), min(all_ys), max(all_xs), max(all_ys)
 
     def get_noun_id(self) -> int:
-        """Generate a new numerical ID for the noun box/wire.
+        """Get the latest available numerical ID for the noun wire.
 
         Returns
         -------
         noun_id : int
-            returns a unique identifier for the noun wire/box
-        """
+            The latest noun wire ID.
 
+        """
         # Increment and return the next available ID
         noun_id = self.noun_id_counter
         self.noun_id_counter += 1
diff --git a/lambeq/backend/drawing/drawing.py b/lambeq/backend/drawing/drawing.py
index a121024d..39f9218a 100644
--- a/lambeq/backend/drawing/drawing.py
+++ b/lambeq/backend/drawing/drawing.py
@@ -44,7 +44,8 @@
                                                     WIRE_COLORS)
 from lambeq.backend.drawing.helpers import drawn_as_spider, needs_asymmetry
 from lambeq.backend.drawing.mat_backend import (
-    MatBackend, WIRE_LINEWIDTH as MAT_WIRE_LINEWIDTH
+    BOX_LINEWIDTH as MAT_BOX_LINEWIDTH, MatBackend,
+    WIRE_LINEWIDTH as MAT_WIRE_LINEWIDTH
 )
 from lambeq.backend.drawing.text_printer import PregroupTextPrinter
 from lambeq.backend.drawing.tikz_backend import (
@@ -135,15 +136,16 @@ def draw(diagram: Diagram, **params) -> None:
     elif params.get('to_tikz', False):
         backend = TikzBackend(
             use_tikzstyles=params.get('use_tikzstyles', None),
+            box_linewidth=params.get('box_linewidth', TIKZ_BOX_LINEWIDTH),
             wire_linewidth=params.get('wire_linewidth',
                                       TIKZ_WIRE_LINEWIDTH),
-            box_linewidth=params.get('box_linewidth', TIKZ_BOX_LINEWIDTH)
         )
     else:
         backend = MatBackend(
             figsize=params.get('figsize', None),
+            box_linewidth=params.get('box_linewidth', MAT_BOX_LINEWIDTH),
             wire_linewidth=params.get('wire_linewidth',
-                                      MAT_WIRE_LINEWIDTH)
+                                      MAT_WIRE_LINEWIDTH),
         )
 
     min_size = 0.01
diff --git a/lambeq/backend/drawing/tikz_backend.py b/lambeq/backend/drawing/tikz_backend.py
index f6d54d96..2eb5477d 100644
--- a/lambeq/backend/drawing/tikz_backend.py
+++ b/lambeq/backend/drawing/tikz_backend.py
@@ -23,9 +23,9 @@
 from math import sqrt
 
 from lambeq.backend.drawing.drawable import DrawableDiagram
-from lambeq.backend.drawing.drawing_backend import (COLORS, DrawingBackend,
-                                                    WIRE_COLORS_NAMES
-                                                    )
+from lambeq.backend.drawing.drawing_backend import (
+    COLORS, DrawingBackend, WIRE_COLORS_NAMES
+)
 from lambeq.backend.drawing.helpers import drawn_as_spider
 from lambeq.backend.grammar import Spider
 
@@ -38,8 +38,8 @@ class TikzBackend(DrawingBackend):
     """ Tikz drawing backend. """
 
     def __init__(self, use_tikzstyles: bool = False,
-                 wire_linewidth: float = WIRE_LINEWIDTH,
-                 box_linewidth: float = BOX_LINEWIDTH):
+                 box_linewidth: float = BOX_LINEWIDTH,
+                 wire_linewidth: float = WIRE_LINEWIDTH):
         self.use_tikzstyles = use_tikzstyles
         self.node_styles: list[str] = []
         self.edge_styles: list[str] = []