class_docstr: Parametric ReLU function as a link.
  
  Args:
      shape (tuple of ints): Shape of the parameter array.
      init (float): Initial parameter value.
  
  See the paper for details: `Delving Deep into Rectifiers: Surpassing
  Human-Level Performance on ImageNet Classification
  <https://arxiv.org/abs/1502.01852>`_.
  
  To try PReLU instead of ReLU, replace ``F.relu`` with individual ``PReLU``
  links registered to the model. For example, the model defined in the
  `MNIST example
  <https://github.com/chainer/chainer/tree/master/examples/mnist/train_mnist.py>`_
  can be rewritten as follows.
  
  ReLU version (original)::
  
      class MLP(chainer.Chain):
  
          def __init__(self, n_units, n_out):
              super(MLP, self).__init__()
              with self.init_scope():
                  self.l1 = L.Linear(None, n_units)
                  self.l2 = L.Linear(None, n_units)
                  self.l3 = L.Linear(None, n_out)
  
          def forward(self, x):
              h1 = F.relu(self.l1(x))
              h2 = F.relu(self.l2(h1))
              return self.l3(h2)
  
  PReLU version::
  
      class MLP(chainer.Chain):
  
          def __init__(self, n_units, n_out):
              super(MLP, self).__init__()
              with self.init_scope():
                  self.l1 = L.Linear(None, n_units)
                  self.a1 = L.PReLU()
                  self.l2 = L.Linear(None, n_units)
                  self.a2 = L.PReLU()
                  self.l3 = L.Linear(None, n_out)
  
          def forward(self, x):
              h1 = self.a1(self.l1(x))
              h2 = self.a2(self.l2(h1))
              return self.l3(h2)
  
  .. seealso:: :func:`chainer.functions.prelu`
  
  Attributes:
      W (~chainer.Parameter): Coefficient of parametric ReLU.
docstr: Applies the parametric ReLU activation function.
  
  Args:
      x (~chainer.Variable): Input variable.
  
  Returns:
      ~chainer.Variable: Output of the parametric ReLU function.
docstr_tok: ["Applies","the","parametric","ReLU","activation","function",".","Args",":","x","(","~chainer.Variable",")",":","Input","variable",".","Returns",":","~chainer.Variable",":","Output","of","the","parametric","ReLU","function","."]
code: def forward(self, x):
      """Applies the parametric ReLU activation function.
  
      Args:
          x (~chainer.Variable): Input variable.
  
      Returns:
          ~chainer.Variable: Output of the parametric ReLU function.
  
      """
      return prelu.prelu(x, self.W)
  
imports: from chainer.functions.activation import prelu
  from chainer import link
  from chainer import variable
label_desc: Use image node_id 2 for calling the PReLU obj's underlying member method code with example usage: obj.forward(x) and returns: prelu