code: def training_step(self, batch, batch_nb):
      x, y = batch
      y_hat = self(x)
      loss = F.mse_loss(y_hat, y)
      self._loss.append(loss.item())
      return {"loss": loss}
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from lightning.pytorch.core.module import LightningModule
  from torch.utils.data import DataLoader
  from tests_pytorch import _PATH_DATASETS
  from tests_pytorch.helpers.datasets import MNIST, AverageDataset, TrialMNIST
label_desc: Use image node_id 3 for calling the ParityModuleRNN obj's underlying member method code with example usage: obj.training_step(batch, batch_nb) and returns: dict