code: def main():
      inputs = tf.Variable(
          tf.reshape(tf.range(0.0, 4.0), [4, 1]), name="input"
      )
      inputs = tf.identity(inputs, "input_node")
      W1 = tf.Variable(tf.zeros([1, 10]) + 0.2)
      b1 = tf.Variable(tf.zeros([10]) + 0.1)
      out1 = tf.nn.bias_add(tf.matmul(inputs, W1), b1)
  
      W2 = tf.Variable(tf.zeros([1, 10]) + 0.2)
      b2 = tf.Variable(tf.zeros([10]) + 0.1)
      out2 = tf.nn.bias_add(tf.matmul(inputs, W2), b2)
  
      with tf.control_dependencies([inputs]):
          output = tf.add_n([out1, out2])
  
      named_output = tf.nn.relu(output, 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], "control_dep", argv[3] == "True")
  
imports: import tensorflow
  from sys import argv
  from util import run_model
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types