class_docstr: FusedRNNCell implementation of LSTM.
  
  This is an extremely efficient LSTM implementation, that uses a single TF op
  for the entire LSTM. It should be both faster and more memory-efficient than
  LSTMBlockCell defined above.
  
  The implementation is based on: http://arxiv.org/abs/1409.2329.
  
  We add forget_bias (default: 1) to the biases of the forget gate in order to
  reduce the scale of forgetting in the beginning of the training.
  
  The variable naming is consistent with `rnn_cell_impl.LSTMCell`.
docstr: Number of units in this cell (output dimension).
docstr_tok: ["Number","of","units","in","this","cell","(","output","dimension",")","."]
code: def num_units(self):
      """Number of units in this cell (output dimension)."""
      return self._num_units
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.keras.engine import input_spec
  from tensorflow.python.layers import base
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_rnn_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
label_desc: Use image node_id 2 for calling the LSTMBlockFusedCell obj's underlying member method code with example usage: obj.num_units() and returns: self