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.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.
class_docstr_tok: ["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.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 = tf.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.LSTMStateTuple(cs, h)
      return h, new_state
  
imports: import tensorflow.compat.v1
  import tf_slim
  from tensorflow.python.ops import gen_nn_ops
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