code: def evaluate(data_loader, model, criterion, ap, global_step, epoch):
      model.eval()
      epoch_time = 0
      keep_avg = KeepAverage()
      c_logger.print_eval_start()
      if data_loader is not None:
          for num_iter, data in enumerate(data_loader):
              start_time = time.time()
  
              # format data
              (
                  text_input,
                  text_lengths,
                  mel_targets,
                  mel_lengths,
                  speaker_c,
                  _,
                  _,
                  _,
                  dur_target,
                  _,
              ) = format_data(data)
  
              # forward pass model
              with torch.cuda.amp.autocast(enabled=c.mixed_precision):
                  (
                      decoder_output,
                      dur_output,
                      alignments,
                  ) = model.forward(
                      text_input,
                      text_lengths,
                      mel_lengths,
                      dur_target,
                      g=speaker_c,
                  )
  
                  # compute loss
                  loss_dict = criterion(
                      decoder_output,
                      mel_targets,
                      mel_lengths,
                      dur_output,
                      torch.log(1 + dur_target),
                      text_lengths,
                  )
  
              # step time
              step_time = time.time() - start_time
              epoch_time += step_time
  
              # compute alignment score
              align_error = 1 - alignment_diagonal_score(
                  alignments, binary=True
              )
              loss_dict["align_error"] = align_error
  
              # aggregate losses from processes
              if num_gpus > 1:
                  loss_dict["loss_l1"] = reduce_tensor(
                      loss_dict["loss_l1"].data, num_gpus
                  )
                  loss_dict["loss_ssim"] = reduce_tensor(
                      loss_dict["loss_ssim"].data, num_gpus
                  )
                  loss_dict["loss_dur"] = reduce_tensor(
                      loss_dict["loss_dur"].data, num_gpus
                  )
                  loss_dict["loss"] = reduce_tensor(
                      loss_dict["loss"].data, num_gpus
                  )
  
              # detach loss values
              loss_dict_new = dict()
              for key, value in loss_dict.items():
                  if isinstance(value, (int, float)):
                      loss_dict_new[key] = value
                  else:
                      loss_dict_new[key] = value.item()
              loss_dict = loss_dict_new
  
              # update avg stats
              update_train_values = dict()
              for key, value in loss_dict.items():
                  update_train_values["avg_" + key] = value
              keep_avg.update_values(update_train_values)
  
              if c.print_eval:
                  c_logger.print_eval_step(
                      num_iter, loss_dict, keep_avg.avg_values
                  )
  
          if args.rank == 0:
              # Diagnostic visualizations
              idx = np.random.randint(mel_targets.shape[0])
              pred_spec = (
                  decoder_output[idx].detach().data.cpu().numpy().T
              )
              gt_spec = mel_targets[idx].data.cpu().numpy().T
              align_img = alignments[idx].data.cpu()
  
              eval_figures = {
                  "prediction": plot_spectrogram(
                      pred_spec, ap, output_fig=False
                  ),
                  "ground_truth": plot_spectrogram(
                      gt_spec, ap, output_fig=False
                  ),
                  "alignment": plot_alignment(
                      align_img, output_fig=False
                  ),
              }
  
              # Sample audio
              eval_audio = ap.inv_melspectrogram(pred_spec.T)
              tb_logger.tb_eval_audios(
                  global_step,
                  {"ValAudio": eval_audio},
                  c.audio["sample_rate"],
              )
  
              # Plot Validation Stats
              tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
              tb_logger.tb_eval_figures(global_step, eval_figures)
  
      if args.rank == 0 and epoch >= c.test_delay_epochs:
          if c.test_sentences_file is None:
              test_sentences = [
                  "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
                  "Be a voice, not an echo.",
                  "I'm sorry Dave. I'm afraid I can't do that.",
                  "This cake is great. It's so delicious and moist.",
                  "Prior to November 22, 1963.",
              ]
          else:
              with open(c.test_sentences_file, "r") as f:
                  test_sentences = [s.strip() for s in f.readlines()]
  
          # test sentences
          test_audios = {}
          test_figures = {}
          print(" | > Synthesizing test sentences")
          if c.use_speaker_embedding:
              if c.use_external_speaker_embedding_file:
                  speaker_embedding = speaker_mapping[
                      list(speaker_mapping.keys())[
                          randrange(len(speaker_mapping) - 1)
                      ]
                  ]["embedding"]
                  speaker_id = None
              else:
                  speaker_id = 0
                  speaker_embedding = None
          else:
              speaker_id = None
              speaker_embedding = None
  
          style_wav = c.get("style_wav_for_test")
          for idx, test_sentence in enumerate(test_sentences):
              try:
                  wav, alignment, _, postnet_output, _, _ = synthesis(
                      model,
                      test_sentence,
                      c,
                      use_cuda,
                      ap,
                      speaker_id=speaker_id,
                      speaker_embedding=speaker_embedding,
                      style_wav=style_wav,
                      truncated=False,
                      enable_eos_bos_chars=c.enable_eos_bos_chars,  # pylint: disable=unused-argument
                      use_griffin_lim=True,
                      do_trim_silence=False,
                  )
  
                  file_path = os.path.join(AUDIO_PATH, str(global_step))
                  os.makedirs(file_path, exist_ok=True)
                  file_path = os.path.join(
                      file_path, "TestSentence_{}.wav".format(idx)
                  )
                  ap.save_wav(wav, file_path)
                  test_audios["{}-audio".format(idx)] = wav
                  test_figures[
                      "{}-prediction".format(idx)
                  ] = plot_spectrogram(postnet_output, ap)
                  test_figures[
                      "{}-alignment".format(idx)
                  ] = plot_alignment(alignment)
              except:  # pylint: disable=bare-except
                  print(" !! Error creating Test Sentence -", idx)
                  traceback.print_exc()
          tb_logger.tb_test_audios(
              global_step, test_audios, c.audio["sample_rate"]
          )
          tb_logger.tb_test_figures(global_step, test_figures)
      return keep_avg.avg_values
  
imports: import argparse
  import glob
  import os
  import sys
  import time
  import traceback
  import numpy
  from random import randrange
  import torch
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  from TTS.tts.datasets.preprocess import load_meta_data
  from TTS.tts.datasets.TTSDataset import MyDataset
  from TTS.tts.layers.losses import SpeedySpeechLoss
  from TTS.tts.utils.generic_utils import check_config_tts, setup_model
  from TTS.tts.utils.io import save_best_model, save_checkpoint
  from TTS.tts.utils.measures import alignment_diagonal_score
  from TTS.tts.utils.speakers import parse_speakers
  from TTS.tts.utils.synthesis import synthesis
  from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
  from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
  from TTS.utils.audio import AudioProcessor
  from TTS.utils.console_logger import ConsoleLogger
  from TTS.utils.distribute import init_distributed, reduce_tensor
  from TTS.utils.generic_utils import KeepAverage, count_parameters, create_experiment_folder, get_git_branch, remove_experiment_folder, set_init_dict
  from TTS.utils.io import copy_model_files, load_config
  from TTS.utils.radam import RAdam
  from TTS.utils.tensorboard_logger import TensorboardLogger
  from TTS.utils.training import NoamLR, setup_torch_training_env
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(data_loader, model, criterion, ap, global_step, epoch) and returns: keep_avg