code: def generate_random_iterable_dataloader(
      num_samples: int, input_dim: int, batch_size: int
  ) -> DataLoader:
      return DataLoader(
          dataset=RandomIterableDataset(input_dim, num_samples),
          batch_size=batch_size,
      )
  
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 5 for calling a global function with example usage: generate_random_iterable_dataloader(num_samples, input_dim, batch_size) and returns: DataLoader