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: Create a cell with added input and/or output dropout.
  
  Dropout is never used on the state.
  
  Arguments:
    cell: an RNNCell, a projection to output_size is added to it.
    input_keep_prob: unit Tensor or float between 0 and 1, input keep
      probability; if it is float and 1, no input dropout will be added.
    output_keep_prob: unit Tensor or float between 0 and 1, output keep
      probability; if it is float and 1, no output dropout will be added.
    seed: (optional) integer, the randomness seed.
  
  Raises:
    TypeError: if cell is not an RNNCell.
    ValueError: if keep_prob is not between 0 and 1.
code: def __init__(
      self, cell, input_keep_prob=1.0, output_keep_prob=1.0, seed=None
  ):
      """Create a cell with added input and/or output dropout.
  
      Dropout is never used on the state.
  
      Arguments:
        cell: an RNNCell, a projection to output_size is added to it.
        input_keep_prob: unit Tensor or float between 0 and 1, input keep
          probability; if it is float and 1, no input dropout will be added.
        output_keep_prob: unit Tensor or float between 0 and 1, output keep
          probability; if it is float and 1, no output dropout will be added.
        seed: (optional) integer, the randomness seed.
  
      Raises:
        TypeError: if cell is not an RNNCell.
        ValueError: if keep_prob is not between 0 and 1.
      """
      if not isinstance(cell, core_rnn_cell.RNNCell):
          raise TypeError("The parameter cell is not a RNNCell.")
      if isinstance(input_keep_prob, float) and not (
          input_keep_prob >= 0.0 and input_keep_prob <= 1.0
      ):
          raise ValueError(
              "Parameter input_keep_prob must be between 0 and 1: %d"
              % input_keep_prob
          )
      if isinstance(output_keep_prob, float) and not (
          output_keep_prob >= 0.0 and output_keep_prob <= 1.0
      ):
          raise ValueError(
              "Parameter output_keep_prob must be between 0 and 1: %d"
              % output_keep_prob
          )
      self._cell = cell
      self._input_keep_prob = input_keep_prob
      self._output_keep_prob = output_keep_prob
      self._seed = seed
  
imports: from __future__ import division, print_function, absolute_import
  import logging
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.ops import array_ops
  from tensorflow.python.util.nest import is_sequence
  from ..None import config
  from ..None import utils
  from ..None import activations
  from ..None import initializations
  from ..None import variables
  from .normalization import batch_normalization
label_desc: Use image node_id 1 to create a new DropoutWrapper object from inherited base classes: core_rnn_cell with example: obj = DropoutWrapper(cell, input_keep_prob, output_keep_prob, seed)