apply Black 25.11.0 style in fbcode (72/92)

Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: itamaro

Differential Revision: D90476322

fbshipit-source-id: e689aa40511799087010490a2ebe5260d41130c8

Author: bowiechen

pearl/action_representation_modules/action_representation_module.py

@@ -10,7 +10,6 @@
 from abc import ABC, abstractmethod
 
 import torch
-
 import torch.nn as nn
 
 

pearl/action_representation_modules/binary_action_representation_module.py

@@ -10,7 +10,6 @@
 from typing import List
 
 import torch
-
 from pearl.action_representation_modules.action_representation_module import (
     ActionRepresentationModule,
 )

pearl/action_representation_modules/one_hot_action_representation_module.py

@@ -11,7 +11,6 @@
 
 import torch
 import torch.nn.functional as F
-
 from pearl.action_representation_modules.action_representation_module import (
     ActionRepresentationModule,
 )

pearl/api/action_result.py

@@ -11,9 +11,7 @@
 from typing import Any
 
 from pearl.api.action_space import ActionSpace
-
 from pearl.api.observation import Observation
-
 from pearl.api.reward import Reward
 
 

pearl/api/space.py

@@ -12,7 +12,6 @@
 from abc import ABC, abstractmethod
 
 import torch
-
 from pearl.api.action import Action
 from torch import Tensor
 

pearl/history_summarization_modules/history_summarization_module.py

@@ -11,7 +11,6 @@
 
 import torch
 import torch.nn as nn
-
 from pearl.api.action import Action
 from pearl.api.history import History
 from pearl.api.observation import Observation

pearl/history_summarization_modules/identity_history_summarization_module.py

@@ -10,7 +10,6 @@
 from typing import List
 
 import torch
-
 from pearl.api.action import Action
 from pearl.api.history import History
 from pearl.api.observation import Observation

pearl/history_summarization_modules/transformer_history_summarization_module.py

@@ -222,9 +222,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         x: [B, T, input_dim] — batch of sequences of (action, observation) pairs.
         Returns: [B, d_model] — representation of the last token for each sequence.
         """
-        assert (
-            x.dim() == 3 and x.size(-1) == self.input_dim
-        ), f"Expected [B, T, {self.input_dim}], got {tuple(x.shape)}"
+        assert x.dim() == 3 and x.size(-1) == self.input_dim, (
+            f"Expected [B, T, {self.input_dim}], got {tuple(x.shape)}"
+        )
 
         return self._encode_last_token(x)
 

pearl/neural_networks/common/utils.py

@@ -13,9 +13,7 @@
 
 import torch
 import torch.nn as nn
-
 from pearl.neural_networks.common.residual_wrapper import ResidualWrapper
-
 from torch.func import stack_module_state
 
 

pearl/neural_networks/common/value_networks.py

@@ -16,7 +16,6 @@
 
 import torch
 import torch.nn as nn
-
 from pearl.neural_networks.common.utils import (
     compute_output_dim_model_cnn,
     conv_block,