class_docstr: Basic LSTM recurrent network cell.
  
  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.
  
  Unlike `rnn_cell_impl.LSTMCell`, this is a monolithic op and should be much
  faster.  The weight and bias matrices should be compatible as long as the
  variable scope matches.
code: def output_size(self):
      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 3 for calling the LSTMBlockCell obj's underlying member method code with example usage: obj.output_size() and returns: self