code: def __init__(
      self,
      module: torch.nn.Module,
      optimizer: torch.optim.Optimizer,
      lr_scheduler: TLRScheduler,
      device: torch.device,
      train_accuracy: BinaryAccuracy,
      tb_logger: TensorBoardLogger,
      log_every_n_steps: int,
  ) -> None:
      super().__init__()
      self.module = module
      self.optimizer = optimizer
      self.lr_scheduler = lr_scheduler
      self.device = device
  
      # create an accuracy Metric to compute the accuracy of training
      self.train_accuracy = train_accuracy
      self.log_every_n_steps = log_every_n_steps
  
      self.tb_logger = tb_logger
  
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.dataset import Dataset, TensorDataset
  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 1 to create a new MyTrainUnit object from inherited base classes: TrainUnit with example: obj = MyTrainUnit(module, optimizer, lr_scheduler, device, train_accuracy, tb_logger, log_every_n_steps)