code: def __init__(
      self,
      tb_logger: TensorBoardLogger,
      opt_cfg: OptimizerConfig,
      log_every_n_steps: int,
      module: torch.nn.Module,
      device: torch.device,
      strategy: str,
      gradient_accumulation_steps: int,
      detect_anomaly: bool,
      clip_grad_norm: float,
  ) -> None:
      super().__init__(
          module=module,
          device=device,
          strategy=strategy,
          gradient_accumulation_steps=gradient_accumulation_steps,
          detect_anomaly=detect_anomaly,
          clip_grad_norm=clip_grad_norm,
      )
      self.tb_logger = tb_logger
      self.opt_cfg = opt_cfg
      self.log_every_n_steps = log_every_n_steps
  
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 1 to create a new MinGPTUnit object from inherited base classes: AutoUnit with example: obj = MinGPTUnit(tb_logger, opt_cfg, log_every_n_steps, module, device, strategy, gradient_accumulation_steps, detect_anomaly, clip_grad_norm)