code: def train_step(self, state: State, data: Batch) -> None:
      data = copy_data_to_device(data, self.device)
      inputs, targets = data
  
      # convert targets to float Tensor for binary_cross_entropy_with_logits
      targets = targets.float()
  
      outputs = self.module(inputs)
      outputs = torch.squeeze(outputs)
      loss = torch.nn.functional.binary_cross_entropy_with_logits(
          outputs, targets
      )
      loss.backward()
  
      self.optimizer.step()
      self.optimizer.zero_grad()
  
      # update metrics & logs
      self.train_accuracy.update(outputs, targets)
      step_count = self.train_progress.num_steps_completed
      if (step_count + 1) % self.log_every_n_steps == 0:
          accuracy = self.train_accuracy.compute()
          self.tb_logger.log("loss", loss, step_count)
          self.tb_logger.log("accuracy", accuracy, step_count)
  
imports: import argparse
  import logging
  import sys
  import tempfile
  from argparse import Namespace
  from typing import List, Tuple
  import torch
  import torch.nn
  from torch.utils.data.datapipes.map.combinatorics import ShufflerIterDataPipe
  from torchdata.dataloader2 import DataLoader2, MultiProcessingReadingService
  from torchdata.datapipes.iter import IterableWrapper
  from torcheval.metrics import BinaryAccuracy
  from torchtnt.framework.state import State
  from torchtnt.framework.train import train
  from torchtnt.framework.unit import TrainUnit
  from torchtnt.utils import copy_data_to_device, init_from_env, seed, TLRScheduler
  from torchtnt.utils.loggers import TensorBoardLogger
label_desc: Use image node_id 2 for calling the MyTrainUnit obj's underlying member method code with example usage: obj.train_step(state, data) without return types