code: def evaluate(model, criterion, ap, global_step, epoch):
      # create train loader
      data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
      model.eval()
      epoch_time = 0
      keep_avg = KeepAverage()
      end_time = time.time()
      c_logger.print_eval_start()
      with torch.no_grad():
          for num_iter, data in enumerate(data_loader):
              start_time = time.time()
              # format data
              x_input, mels, y_coarse = format_data(data)
              loader_time = time.time() - end_time
              global_step += 1
  
              y_hat = model(x_input, mels)
              if isinstance(model.mode, int):
                  y_hat = y_hat.transpose(1, 2).unsqueeze(-1)
              else:
                  y_coarse = y_coarse.float()
              y_coarse = y_coarse.unsqueeze(-1)
              loss = criterion(y_hat, y_coarse)
              # Compute avg loss
              # if num_gpus > 1:
              #     loss = reduce_tensor(loss.data, num_gpus)
              loss_dict = dict()
              loss_dict["model_loss"] = loss.item()
  
              step_time = time.time() - start_time
              epoch_time += step_time
  
              # update avg stats
              update_eval_values = dict()
              for key, value in loss_dict.items():
                  update_eval_values["avg_" + key] = value
              update_eval_values["avg_loader_time"] = loader_time
              update_eval_values["avg_step_time"] = step_time
              keep_avg.update_values(update_eval_values)
  
              # print eval stats
              if c.print_eval:
                  c_logger.print_eval_step(
                      num_iter, loss_dict, keep_avg.avg_values
                  )
  
      if epoch % c.test_every_epochs == 0 and epoch != 0:
          # synthesize a full voice
          rand_idx = random.randrange(0, len(eval_data))
          wav_path = (
              eval_data[rand_idx]
              if not isinstance(eval_data[rand_idx], (tuple, list))
              else eval_data[rand_idx][0]
          )
          wav = ap.load_wav(wav_path)
          ground_mel = ap.melspectrogram(wav)
          sample_wav = model.generate(
              ground_mel,
              c.batched,
              c.target_samples,
              c.overlap_samples,
              use_cuda,
          )
          predict_mel = ap.melspectrogram(sample_wav)
  
          # Sample audio
          tb_logger.tb_eval_audios(
              global_step,
              {"eval/audio": sample_wav},
              c.audio["sample_rate"],
          )
  
          # compute spectrograms
          figures = {
              "eval/ground_truth": plot_spectrogram(ground_mel.T),
              "eval/prediction": plot_spectrogram(predict_mel.T),
          }
          tb_logger.tb_eval_figures(global_step, figures)
  
      tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
      return keep_avg.avg_values
  
imports: import argparse
  import os
  import sys
  import traceback
  import time
  import glob
  import random
  import torch
  from torch.utils.data import DataLoader
  from TTS.tts.utils.visual import plot_spectrogram
  from TTS.utils.audio import AudioProcessor
  from TTS.utils.radam import RAdam
  from TTS.utils.io import copy_model_files, load_config
  from TTS.utils.training import setup_torch_training_env
  from TTS.utils.console_logger import ConsoleLogger
  from TTS.utils.tensorboard_logger import TensorboardLogger
  from TTS.utils.generic_utils import KeepAverage, count_parameters, create_experiment_folder, get_git_branch, remove_experiment_folder, set_init_dict
  from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset
  from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
  from TTS.vocoder.utils.distribution import discretized_mix_logistic_loss, gaussian_loss
  from TTS.vocoder.utils.generic_utils import setup_wavernn
  from TTS.vocoder.utils.io import save_best_model, save_checkpoint
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(model, criterion, ap, global_step, epoch) and returns: keep_avg