class_docstr: Operator adding an input 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 inputs in time,
  do the projection on this batch-concatenated sequence, then split it.
docstr: Run the input projection and then the cell.
docstr_tok: ["Run","the","input","projection","and","then","the","cell","."]
code: def call(self, inputs, state):
      """Run the input projection and then the cell."""
      # Default scope: "InputProjectionWrapper"
      if self._linear is None:
          self._linear = _Linear(inputs, self._num_proj, True)
      projected = self._linear(inputs)
      if self._activation:
          projected = self._activation(projected)
      return self._cell(projected, 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 InputProjectionWrapper obj's underlying member method code with example usage: obj.call(inputs, state) and returns: self