code: def generate_random_dataset(
      num_samples: int, input_dim: int
  ) -> Dataset[Batch]:
      """Returns a dataset of random inputs and labels for binary classification."""
      data = torch.randn(num_samples, input_dim)
      labels = torch.randint(low=0, high=2, size=(num_samples,))
      return TensorDataset(data, labels)
  
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 3 for calling a global function with example usage: generate_random_dataset(num_samples, input_dim) and returns: TensorDataset