code: def eval_step(self, batch, criterion):
      # ignore masking for more consistent evaluation
      batch["cond_idxs"] = None
      return self.train_step(batch, criterion)
  
imports: from dataclasses import dataclass, field
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.nn
  import torchaudio
  from coqpit import Coqpit
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from trainer.torch import DistributedSampler
  from trainer.trainer_utils import get_optimizer, get_scheduler
  from TTS.tts.configs.xtts_config import XttsConfig
  from TTS.tts.datasets.dataset import TTSDataset
  from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram
  from TTS.tts.layers.xtts.dvae import DiscreteVAE
  from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer
  from TTS.tts.layers.xtts.trainer.dataset import XTTSDataset
  from TTS.tts.models.base_tts import BaseTTS
  from TTS.tts.models.xtts import Xtts, XttsArgs, XttsAudioConfig
  from TTS.utils.io import load_fsspec
label_desc: Use image node_id 9 for calling the GPTTrainer obj's underlying member method code with example usage: obj.eval_step(batch, criterion) and returns: self