code: from __future__ import absolute_import
  
  
  from .activation_maximization import visualize_activation_with_losses
  from .activation_maximization import visualize_activation
  
  from .saliency import visualize_saliency_with_losses
  from .saliency import visualize_saliency
  from .saliency import visualize_cam_with_losses
  from .saliency import visualize_cam
  
  from tensorflow.keras import backend as K
  
  
  def get_num_filters(layer):
      """Determines the number of filters within the given `layer`.
  
      Args:
          layer: The keras layer to use.
  
      Returns:
          Total number of filters within `layer`.
          For `keras.layers.Dense` layer, this is the total number of outputs.
      """
      # Handle layers with no channels.
      if K.ndim(layer.output) == 2:
          return K.int_shape(layer.output)[-1]
  
      channel_idx = 1 if K.image_data_format() == 'channels_first' else -1
      return K.int_shape(layer.output)[channel_idx]
  
  
  def overlay(array1, array2, alpha=0.5):
      """Overlays `array1` onto `array2` with `alpha` blending.
  
      Args:
          array1: The first numpy array.
          array2: The second numpy array.
          alpha: The alpha value of `array1` as overlayed onto `array2`. This value needs to be between [0, 1],
              with 0 being `array2` only to 1 being `array1` only (Default value = 0.5).
  
      Returns:
          The `array1`, overlayed with `array2` using `alpha` blending.
      """
      if alpha < 0. or alpha > 1.:
          raise ValueError("`alpha` needs to be between [0, 1]")
      if array1.shape != array2.shape:
          raise ValueError('`array1` and `array2` must have the same shapes')
  
      return (array1 * alpha + array2 * (1. - alpha)).astype(array1.dtype)
  
apis: tensorflow.keras.backend.int_shape tensorflow.keras.backend.ndim tensorflow.keras.backend.image_data_format