class_docstr: Dropout regularization.
class_docstr_tok: ["Dropout","regularization","."]
code: def forward_gpu(self, x):
      if (
          chainer.should_use_cudnn(">=auto", 5000)
          and x[0].flags.c_contiguous
          and self.mask is None
          and not self.return_mask
      ):
          self._use_cudnn = True
  
          if hasattr(self, "states"):
              # if we already have a dropout mask,
              # the forward operation is equal to backward.
              return (
                  cuda.get_cudnn_dropout_states().backward(
                      None, x[0], self.dropout_ratio, self.states
                  ),
              )
  
          self.states, y = cuda.get_cudnn_dropout_states().forward(
              None, x[0], self.dropout_ratio
          )
          return (y,)
      else:
          if self.mask is not None:
              y = x[0] * self.mask
          else:
              rand = cuda.cupy.random.rand(
                  *x[0].shape, dtype=numpy.float32
              )
              scale = x[0].dtype.type(1.0 / (1 - self.dropout_ratio))
              self.mask, y = cuda.elementwise(
                  "T x, R r, T scale, T ratio",
                  "T mask, T y",
                  """
                      mask = (r >= ratio) * scale;
                      y = x * mask;
                      """,
                  "dropout_fwd",
              )(x[0], rand, scale, self.dropout_ratio)
          return (y,)
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 4 for calling the Dropout obj's underlying member method code with example usage: obj.forward_gpu(x) and returns: y, y, cuda