class_docstr: Parametric Rectified Linear Unit gradient function.
code: def forward_gpu(self, inputs):
      x, W, gy = inputs
      masked = cuda.elementwise(
          "T x, T cond, T gy",
          "T masked",
          "masked = cond >= 0 ? (T)0 : (T)(x * gy)",
          "prelu_masked",
      )(x, self.cond, gy)
  
      if self.reduce_axes is None:
          gW = masked.copy()
      else:
          gW = masked.sum(axis=self.reduce_axes)
  
      gx = masked  # reuse buffer
      _fwd_kern()(gy, self.cond, W.reshape(self.extended_shape), gx)
      self.retain_inputs((0, 1, 2))
      return gx, gW
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.utils import type_check
label_desc: Use image node_id 4 for calling the PReLUFunctionGrad obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: gx,  gW