code: def __init__(self, input_dim: int) -> None:
      super().__init__()
      # initialize module, loss_fn, & optimizer
      self.module = nn.Linear(input_dim, 2)
      self.loss_fn = nn.CrossEntropyLoss()
      self.optimizer = torch.optim.SGD(
          self.module.parameters(), lr=0.01
      )
  
imports: from typing import Iterable, Iterator, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.utils.data import DataLoader, Dataset, IterableDataset, TensorDataset
  from torchtnt.framework.auto_unit import AutoUnit
  from torchtnt.framework.state import EntryPoint, PhaseState, State
  from torchtnt.framework.unit import EvalUnit, PredictUnit, TrainUnit
  from torchtnt.utils.lr_scheduler import TLRScheduler
label_desc: Use image node_id 1 to create a new DummyTrainUnit object from inherited base classes: TrainUnit with example: obj = DummyTrainUnit(input_dim)