code: def main(_):
      sess = tf.Session()
  
      # Construct the TensorFlow network.
      ph_float = tf.placeholder(tf.float32, name="ph_float")
      x = tf.transpose(ph_float, name="x")
      v = tf.Variable(
          np.array([[-2.0], [-3.0], [6.0]], dtype=np.float32), name="v"
      )
      m = tf.constant(
          np.array([[0.0, 1.0, 2.0], [-4.0, -1.0, 0.0]]),
          dtype=tf.float32,
          name="m",
      )
      y = tf.matmul(m, x, name="y")
      z = tf.matmul(m, v, name="z")
  
      if FLAGS.debug:
          if FLAGS.use_random_config_path:
              _, config_file_path = tempfile.mkstemp(".tfdbg_config")
          else:
              config_file_path = None
          sess = tf_debug.LocalCLIDebugWrapperSession(
              sess,
              ui_type=FLAGS.ui_type,
              config_file_path=config_file_path,
          )
  
      if FLAGS.error == "shape_mismatch":
          print(
              sess.run(
                  y,
                  feed_dict={ph_float: np.array([[0.0], [1.0], [2.0]])},
              )
          )
      elif FLAGS.error == "uninitialized_variable":
          print(sess.run(z))
      elif FLAGS.error == "no_error":
          print(
              sess.run(
                  y, feed_dict={ph_float: np.array([[0.0, 1.0, 2.0]])}
              )
          )
      else:
          raise ValueError("Unrecognized error type: " + FLAGS.error)
  
imports: import argparse
  import sys
  import tempfile
  import numpy
  import tensorflow
  from tensorflow.python import debug
label_desc: Use image node_id 1 for calling a global function with example usage: main(_) without return types