code: def compute_loss(
      self, state: State, data: Batch
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      inputs, targets = data
      outputs = self.module(inputs)
      outputs = torch.squeeze(outputs)
      loss = torch.nn.functional.nll_loss(outputs, targets)
  
      return loss, outputs
  
imports: import argparse
  import sys
  import tempfile
  from argparse import Namespace
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.optim import Adadelta
  from torch.optim.lr_scheduler import StepLR
  from torch.utils.data import DataLoader
  from torcheval.metrics import MulticlassAccuracy
  from torchtnt.framework.auto_unit import AutoUnit, TrainStepResults
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import State
  from torchtnt.utils import init_from_env, seed, TLRScheduler
  from torchtnt.utils.loggers import TensorBoardLogger
  from torchvision import datasets, transforms
label_desc: Use image node_id 3 for calling the MyUnit obj's underlying member method code with example usage: obj.compute_loss(state, data) and returns: loss,  outputs