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_input,
                  mel_lengths,
                  linear_input,
                  stop_targets,
                  speaker_ids,
                  speaker_embeddings,
                  _,
                  _,
              ) = format_data(data)
              assert mel_input.shape[1] % model.decoder.r == 0
  
              # forward pass model
              if (
                  c.bidirectional_decoder
                  or c.double_decoder_consistency
              ):
                  (
                      decoder_output,
                      postnet_output,
                      alignments,
                      stop_tokens,
                      decoder_backward_output,
                      alignments_backward,
                  ) = model(
                      text_input,
                      text_lengths,
                      mel_input,
                      speaker_ids=speaker_ids,
                      speaker_embeddings=speaker_embeddings,
                  )
              else:
                  (
                      decoder_output,
                      postnet_output,
                      alignments,
                      stop_tokens,
                  ) = model(
                      text_input,
                      text_lengths,
                      mel_input,
                      speaker_ids=speaker_ids,
                      speaker_embeddings=speaker_embeddings,
                  )
                  decoder_backward_output = None
                  alignments_backward = None
  
              # set the alignment lengths wrt reduction factor for guided attention
              if mel_lengths.max() % model.decoder.r != 0:
                  alignment_lengths = (
                      mel_lengths
                      + (
                          model.decoder.r
                          - (mel_lengths.max() % model.decoder.r)
                      )
                  ) // model.decoder.r
              else:
                  alignment_lengths = mel_lengths // model.decoder.r
  
              # compute loss
              loss_dict = criterion(
                  postnet_output,
                  decoder_output,
                  mel_input,
                  linear_input,
                  stop_tokens,
                  stop_targets,
                  mel_lengths,
                  decoder_backward_output,
                  alignments,
                  alignment_lengths,
                  alignments_backward,
                  text_lengths,
              )
  
              # step time
              step_time = time.time() - start_time
              epoch_time += step_time
  
              # compute alignment score
              align_error = 1 - alignment_diagonal_score(alignments)
              loss_dict["align_error"] = align_error
  
              # aggregate losses from processes
              if num_gpus > 1:
                  loss_dict["postnet_loss"] = reduce_tensor(
                      loss_dict["postnet_loss"].data, num_gpus
                  )
                  loss_dict["decoder_loss"] = reduce_tensor(
                      loss_dict["decoder_loss"].data, num_gpus
                  )
                  if c.stopnet:
                      loss_dict["stopnet_loss"] = reduce_tensor(
                          loss_dict["stopnet_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_input.shape[0])
              const_spec = postnet_output[idx].data.cpu().numpy()
              gt_spec = (
                  linear_input[idx].data.cpu().numpy()
                  if c.model in ["Tacotron", "TacotronGST"]
                  else mel_input[idx].data.cpu().numpy()
              )
              align_img = alignments[idx].data.cpu().numpy()
  
              eval_figures = {
                  "prediction": plot_spectrogram(
                      const_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
              if c.model in ["Tacotron", "TacotronGST"]:
                  eval_audio = ap.inv_spectrogram(const_spec.T)
              else:
                  eval_audio = ap.inv_melspectrogram(const_spec.T)
              tb_logger.tb_eval_audios(
                  global_step,
                  {"ValAudio": eval_audio},
                  c.audio["sample_rate"],
              )
  
              # Plot Validation Stats
  
              if (
                  c.bidirectional_decoder
                  or c.double_decoder_consistency
              ):
                  align_b_img = (
                      alignments_backward[idx].data.cpu().numpy()
                  )
                  eval_figures["alignment2"] = plot_alignment(
                      align_b_img, output_fig=False
                  )
              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")
          speaker_id = 0 if c.use_speaker_embedding else None
          speaker_embedding = (
              speaker_mapping[
                  list(speaker_mapping.keys())[
                      randrange(len(speaker_mapping) - 1)
                  ]
              ]["embedding"]
              if c.use_external_speaker_embedding_file
              and c.use_speaker_embedding
              else None
          )
          style_wav = c.get("gst_style_input")
          if style_wav is None and c.use_gst:
              # inicialize GST with zero dict.
              style_wav = {}
              print(
                  "WARNING: You don't provided a gst style wav, for this reason we use a zero tensor!"
              )
              for i in range(c.gst["gst_style_tokens"]):
                  style_wav[str(i)] = 0
          style_wav = c.get("gst_style_input")
          for idx, test_sentence in enumerate(test_sentences):
              try:
                  (
                      wav,
                      alignment,
                      decoder_output,
                      postnet_output,
                      stop_tokens,
                      _,
                  ) = 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, output_fig=False
                  )
                  test_figures[
                      "{}-alignment".format(idx)
                  ] = plot_alignment(alignment, output_fig=False)
              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
  from random import randrange
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from TTS.tts.datasets.preprocess import load_meta_data
  from TTS.tts.datasets.TTSDataset import MyDataset
  from TTS.tts.layers.losses import TacotronLoss
  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 DistributedSampler, apply_gradient_allreduce, 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, adam_weight_decay, check_update, gradual_training_scheduler, set_weight_decay, 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