code: def eval_op(batch, hparams, config_name):
      """Define a evaluation op.
  
      Args:
        batch: Batch produced by NSynthReader.
        hparams: Hyperparameters.
        config_name: Name of config module.
  
      Returns:
        eval_op: A complete evaluation op with summaries.
      """
      phase = not (hparams.mag_only or hparams.raw_audio)
  
      config = utils.get_module(
          "baseline.models.ae_configs.%s" % config_name
      )
      if hparams.raw_audio:
          x = batch["audio"]
          # Add height and channel dims
          x = tf.expand_dims(tf.expand_dims(x, 1), -1)
      else:
          x = batch["spectrogram"]
  
      # Define the model
      with tf.name_scope("Model"):
          z = config.encode(x, hparams, is_training=False)
          xhat = config.decode(z, batch, hparams, is_training=False)
  
      # For interpolation
      tf.add_to_collection("x", x)
      tf.add_to_collection("pitch", batch["pitch"])
      tf.add_to_collection("z", z)
      tf.add_to_collection("xhat", xhat)
  
      total_loss = compute_mse_loss(x, xhat, hparams)
  
      # Define the metrics:
      (
          names_to_values,
          names_to_updates,
      ) = slim.metrics.aggregate_metric_map(
          {
              "Loss": slim.metrics.mean(total_loss),
          }
      )
  
      # Define the summaries
      for name, value in names_to_values.items():
          slim.summaries.add_scalar_summary(
              value, name, print_summary=True
          )
  
      # Interpolate
      with tf.name_scope("Interpolation"):
          xhat = config.decode(
              z, batch, hparams, reuse=True, is_training=False
          )
  
          # Linear interpolation
          z_shift_one_example = tf.concat([z[1:], z[:1]], 0)
          z_linear_half = (z + z_shift_one_example) / 2.0
          xhat_linear_half = config.decode(
              z_linear_half,
              batch,
              hparams,
              reuse=True,
              is_training=False,
          )
  
          # Pitch shift
  
          pitch_plus_2 = tf.clip_by_value(batch["pitch"] + 2, 0, 127)
          pitch_minus_2 = tf.clip_by_value(batch["pitch"] - 2, 0, 127)
  
          batch["pitch"] = pitch_minus_2
          xhat_pitch_minus_2 = config.decode(
              z, batch, hparams, reuse=True, is_training=False
          )
          batch["pitch"] = pitch_plus_2
          xhat_pitch_plus_2 = config.decode(
              z, batch, hparams, reuse=True, is_training=False
          )
  
      utils.specgram_summaries(
          x, "Training Examples", hparams, phase=phase
      )
      utils.specgram_summaries(
          xhat, "Reconstructions", hparams, phase=phase
      )
      utils.specgram_summaries(
          x - xhat, "Difference", hparams, audio=False, phase=phase
      )
      utils.specgram_summaries(
          xhat_linear_half, "Linear Interp. 0.5", hparams, phase=phase
      )
      utils.specgram_summaries(
          xhat_pitch_plus_2, "Pitch +2", hparams, phase=phase
      )
      utils.specgram_summaries(
          xhat_pitch_minus_2, "Pitch -2", hparams, phase=phase
      )
  
      return list(names_to_updates.values())
  
file_path: magenta/magenta/models/nsynth/baseline/models/ae.py
imports: from magenta.contrib import training
  from magenta.models.nsynth import utils
  import numpy
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 4 for calling a global function with example usage: eval_op(batch, hparams, config_name) and returns: list