black formatting

src/gfn/containers/replay_buffer.py

@@ -117,13 +117,14 @@ class PrioritizedReplayBuffer(ReplayBuffer):
         p_norm_distance: p-norm distance value to pass to torch.cdist, for the
             determination of novel states.
     """
+
     def __init__(
         self,
         env: Env,
         objects_type: Literal["transitions", "trajectories", "states"],
         capacity: int = 1000,
-        cutoff_distance: float = 0.,
-        p_norm_distance: float = 1.,
+        cutoff_distance: float = 0.0,
+        p_norm_distance: float = 1.0,
     ):
         """Instantiates a prioritized replay buffer.
         Args:
@@ -137,7 +138,7 @@ def __init__(
                 norms are >= 0).
             p_norm_distance: p-norm distance value to pass to torch.cdist, for the
                 determination of novel states.
-    """
+        """
         super().__init__(env, objects_type, capacity)
         self.cutoff_distance = cutoff_distance
         self.p_norm_distance = p_norm_distance

src/gfn/gflownet/detailed_balance.py

@@ -46,13 +46,21 @@ def logF_named_parameters(self):
         try:
             return {k: v for k, v in self.named_parameters() if "logF" in k}
         except KeyError as e:
-            print("logF not found in self.named_parameters. Are the weights tied with PF? {}".format(e))
+            print(
+                "logF not found in self.named_parameters. Are the weights tied with PF? {}".format(
+                    e
+                )
+            )
 
     def logF_parameters(self):
         try:
             return [v for k, v in self.named_parameters() if "logF" in k]
         except KeyError as e:
-            print("logF not found in self.named_parameters. Are the weights tied with PF? {}".format(e))
+            print(
+                "logF not found in self.named_parameters. Are the weights tied with PF? {}".format(
+                    e
+                )
+            )
 
     def get_scores(
         self, env: Env, transitions: Transitions, recalculate_all_logprobs: bool = False

src/gfn/gflownet/flow_matching.py

@@ -30,7 +30,9 @@ class FMGFlowNet(GFlowNet[Tuple[DiscreteStates, DiscreteStates]]):
     def __init__(self, logF: DiscretePolicyEstimator, alpha: float = 1.0):
         super().__init__()
 
-        assert isinstance(logF, DiscretePolicyEstimator), "logF must be a Discrete Policy Estimator"
+        assert isinstance(
+            logF, DiscretePolicyEstimator
+        ), "logF must be a Discrete Policy Estimator"
         self.logF = logF
         self.alpha = alpha
 

src/gfn/gflownet/sub_trajectory_balance.py

@@ -81,13 +81,21 @@ def logF_named_parameters(self):
         try:
             return {k: v for k, v in self.named_parameters() if "logF" in k}
         except KeyError as e:
-            print("logF not found in self.named_parameters. Are the weights tied with PF? {}".format(e))
+            print(
+                "logF not found in self.named_parameters. Are the weights tied with PF? {}".format(
+                    e
+                )
+            )
 
     def logF_parameters(self):
         try:
             return [v for k, v in self.named_parameters() if "logF" in k]
         except KeyError as e:
-            print("logF not found in self.named_parameters. Are the weights tied with PF? {}".format(e))
+            print(
+                "logF not found in self.named_parameters. Are the weights tied with PF? {}".format(
+                    e
+                )
+            )
 
     def cumulative_logprobs(
         self,

src/gfn/gflownet/trajectory_balance.py

@@ -39,7 +39,9 @@ def __init__(
         if isinstance(logZ, float):
             self.logZ = nn.Parameter(torch.tensor(logZ))
         else:
-            assert isinstance(logZ, ScalarEstimator), "logZ must be either float or a ScalarEstimator"
+            assert isinstance(
+                logZ, ScalarEstimator
+            ), "logZ must be either float or a ScalarEstimator"
             self.logZ = logZ
 
         self.log_reward_clip_min = log_reward_clip_min