black line len 79

Author: ifrit98

template/base/miner.py

@@ -201,7 +201,9 @@ def set_weights(self):
             )
 
         except Exception as e:
-            bt.logging.error(f"Failed to set weights on chain with exception: { e }")
+            bt.logging.error(
+                f"Failed to set weights on chain with exception: { e }"
+            )
 
         bt.logging.info(f"Set weights: {chain_weights}")
 

template/base/neuron.py

@@ -91,7 +91,9 @@ def __init__(self, config=None):
         self.check_registered()
 
         # Each miner gets a unique identity (UID) in the network for differentiation.
-        self.uid = self.metagraph.hotkeys.index(self.wallet.hotkey.ss58_address)
+        self.uid = self.metagraph.hotkeys.index(
+            self.wallet.hotkey.ss58_address
+        )
         bt.logging.info(
             f"Running neuron on subnet: {self.config.netuid} with uid {self.uid} using network: {self.subtensor.chain_endpoint}"
         )

template/base/validator.py

@@ -84,12 +84,15 @@ def serve_axon(self):
                 pass
 
         except Exception as e:
-            bt.logging.error(f"Failed to create Axon initialize with exception: {e}")
+            bt.logging.error(
+                f"Failed to create Axon initialize with exception: {e}"
+            )
             pass
 
     async def concurrent_forward(self):
         coroutines = [
-            self.forward() for _ in range(self.config.neuron.num_concurrent_forwards)
+            self.forward()
+            for _ in range(self.config.neuron.num_concurrent_forwards)
         ]
         await asyncio.gather(*coroutines)
 
@@ -148,7 +151,9 @@ def run(self):
         # In case of unforeseen errors, the validator will log the error and continue operations.
         except Exception as err:
             bt.logging.error("Error during validation", str(err))
-            bt.logging.debug(print_exception(type(err), err, err.__traceback__))
+            bt.logging.debug(
+                print_exception(type(err), err, err.__traceback__)
+            )
 
     def run_in_background_thread(self):
         """
@@ -268,7 +273,9 @@ def resync_metagraph(self):
         # If so, we need to add new hotkeys and moving averages.
         if len(self.hotkeys) < len(self.metagraph.hotkeys):
             # Update the size of the moving average scores.
-            new_moving_average = torch.zeros((self.metagraph.n)).to(self.device)
+            new_moving_average = torch.zeros((self.metagraph.n)).to(
+                self.device
+            )
             min_len = min(len(self.hotkeys), len(self.scores))
             new_moving_average[:min_len] = self.scores[:min_len]
             self.scores = new_moving_average

template/utils/config.py

@@ -63,7 +63,9 @@ def add_args(cls, parser):
     # Netuid Arg: The netuid of the subnet to connect to.
     parser.add_argument("--netuid", type=int, help="Subnet netuid", default=1)
 
-    neuron_type = "validator" if "miner" not in cls.__name__.lower() else "miner"
+    neuron_type = (
+        "validator" if "miner" not in cls.__name__.lower() else "miner"
+    )
 
     parser.add_argument(
         "--neuron.name",

template/utils/uids.py

@@ -26,7 +26,9 @@ def check_uid_availability(
     return True
 
 
-def get_random_uids(self, k: int, exclude: List[int] = None) -> torch.LongTensor:
+def get_random_uids(
+    self, k: int, exclude: List[int] = None
+) -> torch.LongTensor:
     """Returns k available random uids from the metagraph.
     Args:
         k (int): Number of uids to return.

template/validator/reward.py

@@ -49,6 +49,6 @@ def get_rewards(
     - torch.FloatTensor: A tensor of rewards for the given query and responses.
     """
     # Get all the reward results by iteratively calling your reward() function.
-    return torch.FloatTensor([reward(query, response) for response in responses]).to(
-        self.device
-    )
+    return torch.FloatTensor(
+        [reward(query, response) for response in responses]
+    ).to(self.device)

tests/test_template_validator.py

@@ -66,7 +66,9 @@ def test_dummy_responses(self):
 
         responses = self.neuron.dendrite.query(
             # Send the query to miners in the network.
-            axons=[self.neuron.metagraph.axons[uid] for uid in self.miner_uids],
+            axons=[
+                self.neuron.metagraph.axons[uid] for uid in self.miner_uids
+            ],
             # Construct a dummy query.
             synapse=Dummy(dummy_input=self.neuron.step),
             # All responses have the deserialize function called on them before returning.