class_docstr: Operator adding dropout to inputs and outputs of the given cell.
class_docstr_tok: ["Operator","adding","dropout","to","inputs","and","outputs","of","the","given","cell","."]
docstr: Performs dropout given the pre-calculated noise tensor.
docstr_tok: ["Performs","dropout","given","the","pre-calculated","noise","tensor","."]
code: def _variational_recurrent_dropout_value(
      self, unused_index, value, noise, keep_prob
  ):
      """Performs dropout given the pre-calculated noise tensor."""
      # uniform [keep_prob, 1.0 + keep_prob)
      random_tensor = keep_prob + noise
  
      # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
      binary_tensor = math_ops.floor(random_tensor)
      ret = math_ops.divide(value, keep_prob) * binary_tensor
      ret.set_shape(value.get_shape())
      return ret
  
imports: import hashlib
  import numbers
  import sys
  import types
  import warnings
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 8 for calling the DropoutWrapperBase obj's underlying member method code with example usage: obj._variational_recurrent_dropout_value(unused_index, value, noise, keep_prob) and returns: ret