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 build(self, inputs_shape):
      if not inputs_shape.dims[1].value:
          raise ValueError(
              "Expecting inputs_shape[1] to be set: %s"
              % str(inputs_shape)
          )
      input_size = inputs_shape.dims[1].value
      self._kernel = self.add_variable(
          self._names["W"],
          [input_size + self._num_units, self._num_units * 4],
      )
      self._bias = self.add_variable(
          self._names["b"],
          [self._num_units * 4],
          initializer=init_ops.constant_initializer(0.0),
      )
      if self._use_peephole:
          self._w_i_diag = self.add_variable(
              self._names["wci"], [self._num_units]
          )
          self._w_f_diag = self.add_variable(
              self._names["wcf"], [self._num_units]
          )
          self._w_o_diag = self.add_variable(
              self._names["wco"], [self._num_units]
          )
  
      self.built = True
  
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 4 for calling the LSTMBlockCell obj's underlying member method code with example usage: obj.build(inputs_shape) without return types