code: def main(_):
      # Create a dummy dataset.
      num_examples = 8
      steps_per_epoch = 2
      input_dims = 3
      output_dims = 1
      xs = np.zeros([num_examples, input_dims])
      ys = np.zeros([num_examples, output_dims])
      dataset = (
          tf.data.Dataset.from_tensor_slices((xs, ys))
          .repeat(num_examples)
          .batch(int(num_examples / steps_per_epoch))
      )
  
      sess = tf.Session()
      if FLAGS.debug:
          # Use the command-line interface (CLI) of tfdbg.
          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,
          )
      elif FLAGS.tensorboard_debug_address:
          # Use the TensorBoard Debugger Plugin (GUI of tfdbg).
          sess = tf_debug.TensorBoardDebugWrapperSession(
              sess, FLAGS.tensorboard_debug_address
          )
      tf.keras.backend.set_session(sess)
  
      # Create a dummy model.
      model = tf.keras.Sequential(
          [tf.keras.layers.Dense(1, input_shape=[input_dims])]
      )
      model.compile(loss="mse", optimizer="sgd")
  
      # Train the model using the dummy dataset created above.
      model.fit(
          dataset, epochs=FLAGS.epochs, steps_per_epoch=steps_per_epoch
      )
  
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