code: def __init__(
      self,
      *,
      tb_logger: TensorBoardLogger,
      train_accuracy: MulticlassAccuracy,
      log_every_n_steps: int,
      lr: float,
      gamma: float,
      module: torch.nn.Module,
      device: torch.device,
      strategy: str,
      precision: Optional[str],
      gradient_accumulation_steps: int,
      detect_anomaly: bool,
      clip_grad_norm: float,
  ) -> None:
      super().__init__(
          module=module,
          device=device,
          strategy=strategy,
          precision=precision,
          gradient_accumulation_steps=gradient_accumulation_steps,
          detect_anomaly=detect_anomaly,
          clip_grad_norm=clip_grad_norm,
      )
      self.tb_logger = tb_logger
      self.lr = lr
      self.gamma = gamma
  
      # create an accuracy Metric to compute the accuracy of training
      self.train_accuracy = train_accuracy
      self.log_every_n_steps = log_every_n_steps
  
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 1 to create a new MyUnit object from inherited base classes: AutoUnit with example: obj = MyUnit()