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: Run the cell with the declared dropouts.
code: def __call__(self, inputs, state, scope=None):
      """Run the cell with the declared dropouts."""
  
      is_training = config.get_training_mode()
  
      if (
          not isinstance(self._input_keep_prob, float)
          or self._input_keep_prob < 1
      ):
          inputs = tf.cond(
              is_training,
              lambda: tf.nn.dropout(
                  inputs, self._input_keep_prob, seed=self._seed
              ),
              lambda: inputs,
          )
      output, new_state = self._cell(inputs, state)
      if (
          not isinstance(self._output_keep_prob, float)
          or self._output_keep_prob < 1
      ):
          output = tf.cond(
              is_training,
              lambda: tf.nn.dropout(
                  output, self._output_keep_prob, seed=self._seed
              ),
              lambda: output,
          )
      return output, new_state
  
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 4 for calling the DropoutWrapper obj's underlying member method code with example usage: obj.__call__(inputs, state, scope) and returns: output,  new_state