code: def training_step(
      self, batch: Sequence["torch.Tensor"], batch_nb: int
  ) -> dict[str, "torch.Tensor"]:
      data, target = batch
      output = self.forward(data)
      loss = F.nll_loss(output, target)
      return {"loss": loss}
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import numpy
  import pytest
  import optuna
  from optuna._imports import try_import
  from optuna.integration import PyTorchLightningPruningCallback
  from optuna.testing.pruners import DeterministicPruner
  from optuna.testing.storages import StorageSupplier
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj.training_step(batch, batch_nb) and returns: dict