code: def evaluate(
      train_dir, eval_dir, config, dataset_fn, num_batches, master=""
  ):
      """Evaluate the model repeatedly."""
      tf.gfile.MakeDirs(eval_dir)
  
      _trial_summary(
          config.hparams,
          config.eval_examples_path or config.tfds_name,
          eval_dir,
      )
      with tf.Graph().as_default():
          model = config.model
          model.build(
              config.hparams,
              config.data_converter.output_depth,
              is_training=False,
          )
  
          eval_op = model.eval(
              **_get_input_tensors(
                  dataset_fn().take(num_batches), config
              )
          )
  
          hooks = [
              tf_slim.evaluation.StopAfterNEvalsHook(num_batches),
              tf_slim.evaluation.SummaryAtEndHook(eval_dir),
          ]
          tf_slim.evaluation.evaluate_repeatedly(
              train_dir,
              eval_ops=eval_op,
              hooks=hooks,
              eval_interval_secs=60,
              master=master,
          )
  
file_path: magenta/magenta/models/music_vae/music_vae_train.py
imports: import os
  from magenta.models.music_vae import configs
  from magenta.models.music_vae import data
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(train_dir, eval_dir, config, dataset_fn, num_batches, master) without return types