class_docstr: Dropout regularization.
class_docstr_tok: ["Dropout","regularization","."]
code: def __init__(self, dropout_ratio, mask=None, return_mask=False):
      if not 0.0 <= dropout_ratio < 1.0:
          raise ValueError("dropout_ratio must be in the range [0, 1)")
      self.dropout_ratio = dropout_ratio
      self.mask = mask
      self.return_mask = return_mask
      self._use_cudnn = False
  
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 1 to create a new Dropout object from inherited base classes: function_node with example: obj = Dropout(dropout_ratio, mask, return_mask)