class_docstr: A context manager that manages the creation and termination of a
  :class:`petastorm.Reader`.
class_docstr_tok: ["A","context","manager","that","manages","the","creation","and","termination","of","a",":","class",":","`","petastorm.Reader","`","."]
docstr: :param parquet_file_url_list: A string specifying the parquet file URL list.
  :param batch_size: The number of items to return per batch. Default ``None``.
      If ``None``, current implementation will set batch size to be 32, in future,
      ``None`` value will denotes auto tuned best value for batch size.
  :param petastorm_reader_kwargs: other arguments for petastorm reader
  :param shuffling_queue_capacity: Queue capacity is passed to the underlying
      :class:`tf.RandomShuffleQueue` instance. If set to 0, no shuffling will be done.
  :param data_loader_fn: function to generate the PyTorch DataLoader.
  
  See `SparkDatasetConverter.make_torch_dataloader()`  for the definitions
  of the other parameters.
code: def __init__(
      self,
      parquet_file_url_list,
      batch_size,
      petastorm_reader_kwargs,
      shuffling_queue_capacity,
      data_loader_fn,
  ):
      """
      :param parquet_file_url_list: A string specifying the parquet file URL list.
      :param batch_size: The number of items to return per batch. Default ``None``.
          If ``None``, current implementation will set batch size to be 32, in future,
          ``None`` value will denotes auto tuned best value for batch size.
      :param petastorm_reader_kwargs: other arguments for petastorm reader
      :param shuffling_queue_capacity: Queue capacity is passed to the underlying
          :class:`tf.RandomShuffleQueue` instance. If set to 0, no shuffling will be done.
      :param data_loader_fn: function to generate the PyTorch DataLoader.
  
      See `SparkDatasetConverter.make_torch_dataloader()`  for the definitions
      of the other parameters.
      """
      self.parquet_file_url_list = parquet_file_url_list
      self.batch_size = batch_size
      self.petastorm_reader_kwargs = petastorm_reader_kwargs
      self.shuffling_queue_capacity = shuffling_queue_capacity
      self.data_loader_fn = data_loader_fn
  
imports: import atexit
  import datetime
  import logging
  import os
  import shutil
  import threading
  import time
  import uuid
  from distutils.version import LooseVersion
  from multiprocessing.pool import ThreadPool
  from typing import List, Any
  import pyspark
  from pyarrow import LocalFileSystem
  from pyspark.sql.session import SparkSession
  from pyspark.sql.types import ArrayType, DoubleType, FloatType
  from six.moves.urllib.parse import urlparse
  from petastorm import make_batch_reader
  from petastorm.fs_utils import FilesystemResolver, get_filesystem_and_path_or_paths, normalize_dir_url
  from fsspec.core import strip_protocol
label_desc: Use image node_id 1 to create a new TorchDatasetContextManager object from inherited base classes: object with example: obj = TorchDatasetContextManager(parquet_file_url_list, batch_size, petastorm_reader_kwargs, shuffling_queue_capacity, data_loader_fn)