Fix code style

keras/src/callbacks/reduce_lr_on_plateau.py

@@ -138,7 +138,7 @@ def on_epoch_end(self, epoch, logs=None):
  self.model.optimizer.learning_rate = new_lr
  if self.verbose > 0:
  io_utils.print_msg(
- f"\nEpoch {epoch +1}: "
+ f"\nEpoch {epoch + 1}: "
  "ReduceLROnPlateau reducing "
  f"learning rate to {new_lr}."
  )

keras/src/layers/convolutional/conv2d.py

@@ -7,9 +7,9 @@ class Conv2D(BaseConv):
  """2D convolution layer.
 
  This layer creates a convolution kernel that is convolved with the layer
- input over a 2D spatial (or temporal) dimension (height and width) to 
- produce a tensor of outputs. If `use_bias` is True, a bias vector is created 
- and added to the outputs. Finally, if `activation` is not `None`, it is 
+ input over a 2D spatial (or temporal) dimension (height and width) to
+ produce a tensor of outputs. If `use_bias` is True, a bias vector is created
+ and added to the outputs. Finally, if `activation` is not `None`, it is
  applied to the outputs as well.
 
  Args:

keras/src/layers/convolutional/conv3d.py

@@ -7,9 +7,9 @@ class Conv3D(BaseConv):
  """3D convolution layer.
 
  This layer creates a convolution kernel that is convolved with the layer
- input over a 3D spatial (or temporal) dimension (width,height and depth) to 
- produce a tensor of outputs. If `use_bias` is True, a bias vector is created 
- and added to the outputs. Finally, if `activation` is not `None`, it is 
+ input over a 3D spatial (or temporal) dimension (width,height and depth) to
+ produce a tensor of outputs. If `use_bias` is True, a bias vector is created
+ and added to the outputs. Finally, if `activation` is not `None`, it is
  applied to the outputs as well.
 
  Args: