code: def on_train_step_end(
      self,
      state: State,
      data: Batch,
      step: int,
      results: TrainStepResults,
  ) -> None:
      loss, outputs = results.loss, results.outputs
      _, targets = data
      self.train_accuracy.update(outputs, targets)
      if step % self.log_every_n_steps == 0:
          accuracy = self.train_accuracy.compute()
          self.tb_logger.log("accuracy", accuracy, step)
          self.tb_logger.log("loss", loss, step)
  
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 4 for calling the MyUnit obj's underlying member method code with example usage: obj.on_train_step_end(state, data, step, results) without return types