code: def _train(self, epochs: int, theta: float, device: str, **kwargs):
      model = self.model.to(device)
  
      for epoch in range(epochs):
          accumulated_goodness = None
          model.train()
          for j, (data, target) in enumerate(self.train_data):
              # TODO: THE IMAGE SHAPE SHOULD NOT BE DEFINED HERE
              data = data.to(device).reshape(-1, 28 * 28)
              target = torch.functional.F.one_hot(
                  target.to(device),
                  num_classes=len(datasets.MNIST.classes),
              )
              _, goodness = model.ff_train(data, target, theta)
              if accumulated_goodness is None:
                  accumulated_goodness = goodness
              else:
                  accumulated_goodness[0] += goodness[0]
                  accumulated_goodness[1] += goodness[1]
          goodness_ratio = (
              accumulated_goodness[0] - accumulated_goodness[1]
          ) / abs(max(accumulated_goodness))
          self.logger.info(f"Epoch {epoch + 1}")
          self.logger.info(
              f"Accumulated goodness: {accumulated_goodness}"
          )
          self.logger.info(f"Goodness ratio: {goodness_ratio}")
          model.eval()
          correct = 0
          with torch.no_grad():
              for data, target in self.test_data:
                  data = data.to(device).reshape(-1, 28 * 28)
                  target = target.to(device)
                  pred, _ = model.positive_eval(data, theta)
                  correct += pred.eq(target.view_as(pred)).sum().item()
          self.logger.info(
              f"Test accuracy: {correct} / 10000 ({correct / 10000 * 100}%)"
          )
  
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 RecurrentForwardForwardTrainer obj's underlying member method code with example usage: obj._train(epochs, theta, device) without return types