class_docstr: Operator adding an output projection to the given cell.
  
  Note: in many cases it may be more efficient to not use this wrapper,
  but instead concatenate the whole sequence of your outputs in time,
  do the projection on this batch-concatenated sequence, then split it
  if needed or directly feed into a softmax.
docstr: Run the cell and output projection on inputs, starting from state.
docstr_tok: ["Run","the","cell","and","output","projection","on","inputs",",","starting","from","state","."]
code: def call(self, inputs, state):
      """Run the cell and output projection on inputs, starting from state."""
      output, res_state = self._cell(inputs, state)
      if self._linear is None:
          self._linear = _Linear(output, self._output_size, True)
      projected = self._linear(output)
      if self._activation:
          projected = self._activation(projected)
      return projected, res_state
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import rnn_cell_impl
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import nest
label_desc: Use image node_id 5 for calling the OutputProjectionWrapper obj's underlying member method code with example usage: obj.call(inputs, state) and returns: projected,  res_state