class_docstr: Parametric Rectified Linear Unit activation layer.
  
  Formula:
  ``` python
  f(x) = alpha * x for x < 0
  f(x) = x for x >= 0
  ```
  where `alpha` is a learned array with the same shape as x.
  
  Args:
      alpha_initializer: Initializer function for the weights.
      alpha_regularizer: Regularizer for the weights.
      alpha_constraint: Constraint for the weights.
      shared_axes: The axes along which to share learnable parameters for the
          activation function. For example, if the incoming feature maps are
          from a 2D convolution with output shape
          `(batch, height, width, channels)`, and you wish to share parameters
          across space so that each filter only has one set of parameters,
          set `shared_axes=[1, 2]`.
      **kwargs: Base layer keyword arguments, such as `name` and `dtype`.
code: def __init__(
      self,
      alpha_initializer="Zeros",
      alpha_regularizer=None,
      alpha_constraint=None,
      shared_axes=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.supports_masking = True
      self.alpha_initializer = initializers.get(alpha_initializer)
      self.alpha_regularizer = regularizers.get(alpha_regularizer)
      self.alpha_constraint = constraints.get(alpha_constraint)
      if shared_axes is None:
          self.shared_axes = None
      elif not isinstance(shared_axes, (list, tuple)):
          self.shared_axes = [shared_axes]
      else:
          self.shared_axes = list(shared_axes)
  
file_path: keras/keras/layers/activations/prelu.py
imports: from keras import activations
  from keras import constraints
  from keras import initializers
  from keras import regularizers
  from keras.api_export import keras_export
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
label_desc: Use image node_id 1 to create a new PReLU object from inherited base classes: Layer with example: obj = PReLU(alpha_initializer, alpha_regularizer, alpha_constraint, shared_axes)