class_docstr: Perform gradients backpropagation for a given input
  
  Paper: [Deep Inside Convolutional Networks: Visualising Image Classification
      Models and Saliency Maps](https://arxiv.org/abs/1312.6034)
class_docstr_tok: ["Perform","gradients","backpropagation","for","a","given","input","Paper",":","[","Deep","Inside","Convolutional","Networks",":","Visualising","Image","Classification","Models","and","Saliency","Maps","]","(","https",":","\/\/arxiv.org\/abs\/1312.6034",")"]
docstr: Check whether the given layer is an activation layer
  
  Args:
      layer (tf.keras.layers.Layer): The layer to check
  
  Returns:
      Whether the layer is an activation layer
docstr_tok: ["Check","whether","the","given","layer","is","an","activation","layer","Args",":","layer","(","tf.keras.layers.Layer",")",":","The","layer","to","check","Returns",":","Whether","the","layer","is","an","activation","layer"]
code: def _is_activation_layer(self, layer):
      """
      Check whether the given layer is an activation layer
  
      Args:
          layer (tf.keras.layers.Layer): The layer to check
  
      Returns:
          Whether the layer is an activation layer
      """
      return isinstance(layer, ACTIVATION_LAYER_CLASSES)
  
file_path: tf-explain/tf_explain/core/vanilla_gradients.py
imports: from warnings import warn
  import tensorflow
  from tf_explain.utils.display import grid_display
  from tf_explain.utils.image import transform_to_normalized_grayscale
  from tf_explain.utils.saver import save_grayscale
label_desc: Use image node_id 3 for calling the VanillaGradients obj's underlying member method code with example usage: obj._is_activation_layer(layer) and returns: isinstance