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 build(self, input_shape):
      param_shape = list(input_shape[1:])
      if self.shared_axes is not None:
          for i in self.shared_axes:
              param_shape[i - 1] = 1
      self.alpha = self.add_weight(
          shape=param_shape,
          name="alpha",
          initializer=self.alpha_initializer,
          regularizer=self.alpha_regularizer,
          constraint=self.alpha_constraint,
      )
      # Set input spec
      axes = {}
      if self.shared_axes:
          for i in range(1, len(input_shape)):
              if i not in self.shared_axes:
                  axes[i] = input_shape[i]
      self.input_spec = InputSpec(ndim=len(input_shape), axes=axes)
      self.built = True
  
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 2 for calling the PReLU obj's underlying member method code with example usage: obj.build(input_shape) without return types