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: Runs the wrapped cell and applies dropout.
  
  Args:
    inputs: A tensor with wrapped cell's input.
    state: A tensor or tuple of tensors with wrapped cell's state.
    cell_call_fn: Wrapped cell's method to use for step computation (cell's
      `__call__` or 'call' method).
    **kwargs: Additional arguments.
  
  Returns:
    A pair containing:
  
    - Output: A tensor with cell's output.
    - New state: A tensor or tuple of tensors with new wrapped cell's state.
docstr_tok: ["Runs","the","wrapped","cell","and","applies","dropout",".","Args",":","inputs",":","A","tensor","with","wrapped","cell","'s","input",".","state",":","A","tensor","or","tuple","of","tensors","with","wrapped","cell","'s","state",".","cell_call_fn",":","Wrapped","cell","'s","method","to","use","for","step","computation","(","cell's","`","__call__","`","or","'call","'","method",")",".","*","*","kwargs",":","Additional","arguments",".","Returns",":","A","pair","containing",":","-","Output",":","A","tensor","with","cell","'s","output",".","-","New","state",":","A","tensor","or","tuple","of","tensors","with","new","wrapped","cell","'s","state","."]
code: def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
      """Runs the wrapped cell and applies dropout.
  
      Args:
        inputs: A tensor with wrapped cell's input.
        state: A tensor or tuple of tensors with wrapped cell's state.
        cell_call_fn: Wrapped cell's method to use for step computation (cell's
          `__call__` or 'call' method).
        **kwargs: Additional arguments.
  
      Returns:
        A pair containing:
  
        - Output: A tensor with cell's output.
        - New state: A tensor or tuple of tensors with new wrapped cell's state.
      """
  
      def _should_dropout(p):
          return (not isinstance(p, float)) or p < 1
  
      if _should_dropout(self._input_keep_prob):
          inputs = self._dropout(
              inputs,
              "input",
              self._recurrent_input_noise,
              self._input_keep_prob,
          )
      output, new_state = cell_call_fn(inputs, state, **kwargs)
      if _should_dropout(self._state_keep_prob):
          # Identify which subsets of the state to perform dropout on and
          # which ones to keep.
          shallow_filtered_substructure = (
              nest.get_traverse_shallow_structure(
                  self._dropout_state_filter, new_state
              )
          )
          new_state = self._dropout(
              new_state,
              "state",
              self._recurrent_state_noise,
              self._state_keep_prob,
              shallow_filtered_substructure,
          )
      if _should_dropout(self._output_keep_prob):
          output = self._dropout(
              output,
              "output",
              self._recurrent_output_noise,
              self._output_keep_prob,
          )
      return output, new_state
  
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 10 for calling the DropoutWrapperBase obj's underlying member method code with example usage: obj._call_wrapped_cell(inputs, state, cell_call_fn) and returns: output,  new_state, unknown