[pre-commit.ci] auto fixes from pre-commit.com hooks

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lerobot/common/policies/dot/modeling_dot.py

@@ -68,6 +68,7 @@ class DOT(nn.Module):
           │                         Outputs                      │
           └──────────────────────────────────────────────────────┘
     """
+
     def __init__(self, config: DOTConfig):
         super().__init__()
         self.config = config
@@ -203,11 +204,15 @@ class DOT(nn.Module):
         inputs_projections += self.inputs_pos_emb.expand(batch_size, -1, -1)
 
         # Prepend a trainable prefix token to the input sequence
-        inputs_projections = torch.cat([self.prefix_input.expand(batch_size, -1, -1), inputs_projections], dim=1) # (B, T+1, D)
+        inputs_projections = torch.cat(
+            [self.prefix_input.expand(batch_size, -1, -1), inputs_projections], dim=1
+        )  # (B, T+1, D)
 
         # Use different positional encodings and masks for training vs. inference.
         if self.training:
-            decoder_out = self.decoder(self.decoder_pos.expand(batch_size, -1, -1), inputs_projections, self.mask)
+            decoder_out = self.decoder(
+                self.decoder_pos.expand(batch_size, -1, -1), inputs_projections, self.mask
+            )
         else:
             decoder_out = self.decoder(self.decoder_pos_inf.expand(batch_size, -1, -1), inputs_projections)
         return self.action_head(decoder_out)
@@ -472,6 +477,7 @@ class LoRAConv2d(nn.Module):
         base_conv (nn.Conv2d): The original convolutional layer to be adapted.
         rank (int): The rank of the low-rank approximation (default: 4).
     """
+
     def __init__(self, base_conv: nn.Conv2d, rank: int = 4):
         super().__init__()
         self.base_conv = base_conv