code: def _train(
      self,
      epochs: int,
      theta: float,
      device: str,
      predicted_tokens: int,
      **kwargs,
  ):
      model = self.model.to(device)
      self.model.epochs = epochs
      self.model.predicted_tokens = predicted_tokens
      token_num = len(VOCABULARY)
      sequence_len = self.model.seq_len
  
      for input_data in self.train_data:
          input_data = torch.functional.F.one_hot(
              input_data[0].to(device), num_classes=token_num
          ).float()
  
          accumulated_goodness = model.LM_ff_train(
              input_data, theta=theta
          )
          goodness_ratio = (
              accumulated_goodness[0] - accumulated_goodness[1]
          ) / abs(max(accumulated_goodness))
          self.logger.info("Trained on batch")
          self.logger.info(
              f"Accumulated goodness: {accumulated_goodness}"
          )
          self.logger.info(
              f"Accumulated goodness ratio: {goodness_ratio}"
          )
  
      for test_data in self.test_data:
          test_data = torch.functional.F.one_hot(
              test_data[0].to(device), num_classes=token_num
          ).float()
          test_data = test_data.reshape(-1, token_num * sequence_len)
          predictions, _ = model.positive_eval(test_data, theta)
          perplexity = compute_perplexity(predictions)
          self.logger.info(f"Perplexity: {perplexity}")
  
imports: from abc import ABC, abstractmethod
  import torch
  from nebullvm.operations.base import Operation
  from nebullvm.operations.fetch_operations.local import FetchModelFromLocal
  from torch.utils.data import DataLoader
  from torchvision import datasets
  from forward_forward.operations.data import VOCABULARY
  from forward_forward.operations.fetch_operations import FetchTrainingDataFromLocal
  from forward_forward.utils.labels import LabelsInjector
  from forward_forward.utils.modules import FCNetFFProgressive
  from forward_forward.utils.utils import ProgressiveTrainingDataset, compute_perplexity
label_desc: Use image node_id 1 for calling the NLPForwardForwardTrainer obj's underlying member method code with example usage: obj._train(epochs, theta, device, predicted_tokens) without return types