code: def train_step(
      self, state: State, data: Batch
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      inputs, targets = data
  
      outputs = self.module(inputs)
      loss = self.loss_fn(outputs, targets)
      loss.backward()
  
      self.optimizer.step()
      self.optimizer.zero_grad()
  
      return loss, outputs
  
imports: from typing import Iterable, Iterator, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.utils.data import DataLoader, Dataset, IterableDataset, TensorDataset
  from torchtnt.framework.auto_unit import AutoUnit
  from torchtnt.framework.state import EntryPoint, PhaseState, State
  from torchtnt.framework.unit import EvalUnit, PredictUnit, TrainUnit
  from torchtnt.utils.lr_scheduler import TLRScheduler
label_desc: Use image node_id 2 for calling the DummyFitUnit obj's underlying member method code with example usage: obj.train_step(state, data) and returns: loss,  outputs