make style

src/diffusers/models/unet_2d_blocks.py

@@ -512,17 +512,24 @@ class UNetMidBlock2D(nn.Module):
         dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
         num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
         resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
-        resnet_time_scale_shift (`str`, *optional*, defaults to `default`): The type of normalization to apply to the time embeddings. This can help to improve the performance of the model on tasks with long-range temporal dependencies.
+        resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
+            The type of normalization to apply to the time embeddings. This can help to improve the performance of the
+            model on tasks with long-range temporal dependencies.
         resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
-        resnet_groups (`int`, *optional*, defaults to 32): The number of groups to use in the group normalization layers of the resnet blocks.
+        resnet_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use in the group normalization layers of the resnet blocks.
         attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
-        resnet_pre_norm (`bool`, *optional*, defaults to `True`): Whether to use pre-normalization for the resnet blocks.
+        resnet_pre_norm (`bool`, *optional*, defaults to `True`):
+            Whether to use pre-normalization for the resnet blocks.
         add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
-        attention_head_dim (`int`, *optional*, defaults to 1): Dimension of a single attention head. The number of attention heads is determined based on this value and the number of input channels.
+        attention_head_dim (`int`, *optional*, defaults to 1):
+            Dimension of a single attention head. The number of attention heads is determined based on this value and
+            the number of input channels.
         output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
 
     Returns:
-        `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, in_channels, height, width)`.
+        `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
+        in_channels, height, width)`.
 
     """