class_docstr: Layer that applies an update to the cost function based input activity.
  
  Args:
      l1: L1 regularization factor (positive float).
      l2: L2 regularization factor (positive float).
  
  Input shape:
      Arbitrary. Use the keyword argument `input_shape`
      (tuple of integers, does not include the samples axis)
      when using this layer as the first layer in a model.
  
  Output shape:
      Same shape as input.
code: def __init__(self, l1=0.0, l2=0.0, **kwargs):
      super().__init__(
          activity_regularizer=regularizers.L1L2(l1=l1, l2=l2), **kwargs
      )
      self.supports_masking = True
      self.l1 = l1
      self.l2 = l2
  
file_path: keras/keras/layers/regularization/activity_regularization.py
imports: from keras import regularizers
  from keras.api_export import keras_export
  from keras.layers.layer import Layer
label_desc: Use image node_id 1 to create a new ActivityRegularization object from inherited base classes: Layer with example: obj = ActivityRegularization(l1, l2)