code: def _RunLSTMLayer(
      self,
      basename,
      init_weights,
      m_init_scalar,
      c_init_scalar,
      pad_scalar,
  ):
      with self.session() as sess:
          num_inputs = 1
          num_nodes = 1
          seq_length = 3
  
          weights = init_weights(
              lstm.LSTMCellWeightsShape(num_inputs, num_nodes)
          )
          m_init = constant_op.constant(
              [[m_init_scalar]] * self._batch_size
          )
          c_init = constant_op.constant(
              [[c_init_scalar]] * self._batch_size
          )
          x_seq = [constant_op.constant(self._inputs)] * seq_length
          pad_seq = [
              constant_op.constant([[pad_scalar]] * self._batch_size)
          ] * seq_length
  
          out_seq = lstm.LSTMLayer(
              "lstm", weights, m_init, c_init, x_seq, pad_seq
          )
          _DumpGraph(
              sess.graph,
              "lstm_layer_%s_%d_%d_%d"
              % (basename, m_init_scalar, c_init_scalar, pad_scalar),
          )
  
          # Initialize variables and run the unrolled LSTM layer.
          self.evaluate(variables.global_variables_initializer())
          return self.evaluate(out_seq)
  
imports: import argparse
  import os
  import sys
  import numpy
  from tensorflow.compiler.tests import lstm
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 7 for calling the LSTMTest obj's underlying member method code with example usage: obj._RunLSTMLayer(basename, init_weights, m_init_scalar, c_init_scalar, pad_scalar) and returns: self