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.
docstr: Long short-term memory cell (LSTM).
docstr_tok: ["Long","short-term","memory","cell","(","LSTM",")","."]
code: def call(self, inputs, state):
      """Long short-term memory cell (LSTM)."""
      if len(state) != 2:
          raise ValueError(
              "Expecting state to be a tuple with length 2."
          )
  
      if self._use_peephole:
          wci = self._w_i_diag
          wcf = self._w_f_diag
          wco = self._w_o_diag
      else:
          wci = wcf = wco = array_ops.zeros(
              [self._num_units], dtype=self.dtype
          )
  
      (cs_prev, h_prev) = state
      (_, cs, _, _, _, _, h) = _lstm_block_cell(
          inputs,
          cs_prev,
          h_prev,
          self._kernel,
          self._bias,
          wci=wci,
          wcf=wcf,
          wco=wco,
          forget_bias=self._forget_bias,
          cell_clip=self._cell_clip,
          use_peephole=self._use_peephole,
      )
  
      new_state = rnn_cell_impl.LSTMStateTuple(cs, h)
      return h, new_state
  
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 5 for calling the LSTMBlockCell obj's underlying member method code with example usage: obj.call(inputs, state) and returns: h,  new_state