code: def compute_loss(
      self, state: State, data: Batch
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      input, target = data
      outputs, loss = self.module(input, target)
      return loss, outputs
  
imports: import argparse
  import logging
  import tempfile
  from argparse import Namespace
  from typing import Optional, Tuple
  import torch
  from libfb.py import parutil
  from torch.utils.data import random_split
  from torch.utils.data.dataset import Dataset, Subset
  from torchtnt.examples.mingpt.char_dataset import CharDataset, DataConfig
  from torchtnt.examples.mingpt.model import create_optimizer, GPT, GPTConfig, OptimizerConfig
  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
label_desc: Use image node_id 3 for calling the MinGPTUnit obj's underlying member method code with example usage: obj.compute_loss(state, data) and returns: loss,  outputs