[DOC] Migrate docstrings in models/nn/rnn.py to numpydoc format

pytorch_forecasting/models/nn/rnn.py

@@ -29,26 +29,36 @@ def handle_no_encoding(
         """
         Mask the hidden_state where there is no encoding.
 
-        Args:
-            hidden_state (HiddenState): hidden state where some entries need replacement
-            no_encoding (torch.BoolTensor): positions that need replacement
-            initial_hidden_state (HiddenState): hidden state to use for replacement
-
-        Returns:
-            HiddenState: hidden state with propagated initial hidden state where appropriate
-        """  # noqa: E501
+        Parameters
+        ----------
+        hidden_state : HiddenState
+            Hidden state where some entries need replacement.
+        no_encoding : torch.BoolTensor
+            Positions that need replacement.
+        initial_hidden_state : HiddenState
+            Hidden state to use for replacement.
+
+        Returns
+        -------
+        HiddenState
+            Hidden state with propagated initial hidden state where appropriate.
+        """
         pass
 
     @abstractmethod
     def init_hidden_state(self, x: torch.Tensor) -> HiddenState:
         """
         Initialise a hidden_state.
 
-        Args:
-            x (torch.Tensor): network input
+        Parameters
+        ----------
+        x : torch.Tensor
+            Network input.
 
-        Returns:
-            HiddenState: default (zero-like) hidden state
+        Returns
+        -------
+        HiddenState
+            Default (zero-like) hidden state.
         """
         pass
 
@@ -59,12 +69,17 @@ def repeat_interleave(
         """
         Duplicate the hidden_state n_samples times.
 
-        Args:
-            hidden_state (HiddenState): hidden state to repeat
-            n_samples (int): number of repetitions
-
-        Returns:
-            HiddenState: repeated hidden state
+        Parameters
+        ----------
+        hidden_state : HiddenState
+            Hidden state to repeat.
+        n_samples : int
+            Number of repetitions.
+
+        Returns
+        -------
+        HiddenState
+            Repeated hidden state.
         """
         pass
 
@@ -80,19 +95,25 @@ def forward(
 
         Functions as normal for RNN. Only changes output if lengths are defined.
 
-        Args:
-            x (Union[rnn.PackedSequence, torch.Tensor]): input to RNN. either packed sequence or tensor of
-                padded sequences
-            hx (HiddenState, optional): hidden state. Defaults to None.
-            lengths (torch.LongTensor, optional): lengths of sequences. If not None, used to determine correct returned
-                hidden state. Can contain zeros. Defaults to None.
-            enforce_sorted (bool, optional): if lengths are passed, determines if RNN expects them to be sorted.
-                Defaults to True.
-
-        Returns:
-            Tuple[Union[rnn.PackedSequence, torch.Tensor], HiddenState]: output and hidden state.
-                Output is packed sequence if input has been a packed sequence.
-        """  # noqa: E501
+        Parameters
+        ----------
+        x : rnn.PackedSequence or torch.Tensor
+            Input to RNN. Either packed sequence or tensor of padded sequences.
+        hx : HiddenState, optional
+            Hidden state. Defaults to None.
+        lengths : torch.LongTensor, optional
+            Lengths of sequences. If not None, used to determine correct returned
+            hidden state. Can contain zeros. Defaults to None.
+        enforce_sorted : bool, optional
+            If lengths are passed, determines if RNN expects them to be sorted.
+            Defaults to True.
+
+        Returns
+        -------
+        tuple of (rnn.PackedSequence or torch.Tensor, HiddenState)
+            Output and hidden state. Output is a packed sequence if input
+            was a packed sequence.
+        """
         if isinstance(x, rnn.PackedSequence) or lengths is None:
             assert (
                 lengths is None
@@ -230,11 +251,15 @@ def get_rnn(cell_type: type[RNN] | str) -> type[RNN]:
     """
     Get LSTM or GRU.
 
-    Args:
-        cell_type (Union[RNN, str]): "LSTM" or "GRU"
+    Parameters
+    ----------
+    cell_type : type[RNN] or str
+        RNN class or string identifier, either ``"LSTM"`` or ``"GRU"``.
 
-    Returns:
-        Type[RNN]: returns GRU or LSTM RNN module
+    Returns
+    -------
+    type[RNN]
+        Returns the GRU or LSTM RNN class.
     """
     if isinstance(cell_type, RNN):
         rnn = cell_type