code: def train(hparams):
      # init exp and track all the parameters from the HyperOptArgumentParser
      exp = Experiment(
          name=hparams.test_tube_exp_name,
          save_dir=hparams.log_path,
          autosave=False,
      )
      exp.argparse(hparams)
  
      # define tensorflow graph
      x = tf.placeholder(dtype=tf.int32, name="x")
      y = tf.placeholder(dtype=tf.int32, name="y")
      out = x * y
  
      sess = tf.Session()
  
      # Run the tf op
      for train_step in range(0, 100):
          output = sess.run(
              out, feed_dict={x: hparams.x_val, y: hparams.y_val}
          )
          exp.log({"fake_err": output})
  
      # save exp when we're done
      exp.save()
  
label_desc: Use image node_id 1 for calling a global function with example usage: train(hparams) without return types