code: def main():
      tf.set_random_seed(1)
      n_steps = 2
      n_input = 10
      n_hidden = 10
  
      xs = tf.Variable(
          tf.random_uniform([4, n_steps, n_input]) + 10,
          name="input",
          dtype=tf.float32,
      )
      xs = tf.identity(xs, name="input_node")
      x = tf.unstack(xs, n_steps, 1)
  
      cell = tf.contrib.rnn.BasicLSTMCell(n_hidden)
      init_state = cell.zero_state(4, tf.float32)
  
      outputs = []
      for i in range(n_steps):
          if i == 0:
              output, state = cell(x[-1], init_state)
          else:
              output, state = cell(output, state)
          outputs.append(output)
  
      final = tf.identity(outputs, name="output")
  
      net_outputs = map(
          lambda x: tf.get_default_graph().get_tensor_by_name(x),
          argv[2].split(","),
      )
      run_model(net_outputs, argv[1], "rnn", argv[3] == "True")
  
imports: import tensorflow
  import numpy
  from sys import argv
  from tensorflow.contrib import rnn
  from util import run_model
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types