code: def __init__(
      self,
      module: torch.nn.Module,
      optimizer: torch.optim.Optimizer,
      device: torch.device,
      tb_logger: TensorBoardLogger,
      train_auroc: BinaryAUROC,
      log_every_n_steps: int,
  ) -> None:
      super().__init__()
      self.module = module
      self.pipeline: TrainPipelineSparseDist = TrainPipelineSparseDist(
          module, optimizer, device, execute_all_batches=True
      )
      self.optimizer = optimizer
      self.device = device
      self.train_auroc = train_auroc
      self.tb_logger = tb_logger
      self.log_every_n_steps = log_every_n_steps
  
imports: import argparse
  import sys
  import tempfile
  import uuid
  from typing import Iterator, List, Optional, Tuple
  import torch
  from pyre_extensions import none_throws
  from torch import distributed
  from torch.distributed import launcher
  from torch.distributed.optim.apply_optimizer_in_backward import _apply_optimizer_in_backward
  from torch.utils.data import DataLoader
  from torcheval.metrics.classification.auroc import BinaryAUROC
  from torcheval.metrics.toolkit import sync_and_compute
  from torchrec import EmbeddingBagCollection
  from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
  from torchrec.datasets.random import RandomRecDataset
  from torchrec.distributed import TrainPipelineSparseDist
  from torchrec.distributed.comm import get_local_size
  from torchrec.distributed.model_parallel import DistributedModelParallel, get_default_sharders
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.distributed.planner.storage_reservations import HeuristicalStorageReservation
  from torchrec.models.dlrm import DLRM, DLRMTrain
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.optim.keyed import KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
  from torchtnt.framework.callbacks import TQDMProgressBar
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import State
  from torchtnt.framework.unit import EvalUnit, TrainUnit
  from torchtnt.utils import get_process_group_backend_from_device, init_from_env, rank_zero_print
  from torchtnt.utils.loggers.tensorboard import TensorBoardLogger
label_desc: Use image node_id 1 to create a new MyUnit object from inherited base classes: TrainUnit, EvalUnit with example: obj = MyUnit(module, optimizer, device, tb_logger, train_auroc, log_every_n_steps)