code: def criteo_kaggle(
      path: str,
      *,
      # pyre-ignore[2]
      row_mapper: Optional[
          Callable[[List[str]], Any]
      ] = _default_row_mapper,
      # pyre-ignore[2]
      **open_kw,
  ) -> IterDataPipe:
      """`Kaggle/Criteo Display Advertising <https://www.kaggle.com/c/criteo-display-ad-challenge/>`_ Dataset
  
      Args:
          path (str): local path to train or test dataset file.
          row_mapper (Optional[Callable[[List[str]], Any]]): function to apply to each split TSV line.
          open_kw: options to pass to underlying invocation of iopath.common.file_io.PathManager.open.
  
      Example::
  
          train_datapipe = criteo_kaggle(
              "/home/datasets/criteo_kaggle/train.txt",
          )
          example = next(iter(train_datapipe))
          test_datapipe = criteo_kaggle(
              "/home/datasets/criteo_kaggle/test.txt",
          )
          example = next(iter(test_datapipe))
      """
      return CriteoIterDataPipe(
          (path,), row_mapper=row_mapper, **open_kw
      )
  
imports: import os
  import shutil
  import time
  from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.utils.data.datapipes
  from iopath.common.file_io import PathManager, PathManagerFactory
  from pyre_extensions import none_throws
  from torch.utils.data import IterableDataset, IterDataPipe
  from torchrec.datasets.utils import Batch, LoadFiles, PATH_MANAGER_KEY, ReadLinesFromCSV, safe_cast
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 3 for calling a global function with example usage: criteo_kaggle(path) and returns: CriteoIterDataPipe