code: 
  import operator_benchmark as op_bench
  import torch
  
  
  """Microbenchmarks for quantized batchnorm operator."""
  
  batchnorm_configs_short = op_bench.config_list(
      attr_names=["M", "N", "K"],
      attrs=[
          [1, 256, 3136],
      ],
      cross_product_configs={
          'device': ['cpu'],
          'dtype': (torch.qint8,),
      },
      tags=["short"]
  )
  
  
  class QBatchNormBenchmark(op_bench.TorchBenchmarkBase):
      def init(self, M, N, K, device, dtype):
          self._init(M, N, K, device)
          x_scale = 0.1
          x_zero_point = 0
          self.q_input_one = torch.quantize_per_tensor(
              self.input_one, scale=x_scale, zero_point=x_zero_point, dtype=dtype)
          self.mean = torch.rand(N)
          self.var = torch.rand(N)
          self.weight = torch.rand(N)
          self.bias = torch.rand(N)
          self.eps = 1e-5
          self.Y_scale = 0.1
          self.Y_zero_point = 0
  
      def _init(self, M, N, K, device):
          pass
  
      def forward(self):
          pass
  
  
  class QBatchNorm1dBenchmark(QBatchNormBenchmark):
      def _init(self, M, N, K, device):
          self.set_module_name("QBatchNorm1d")
          self.input_one = torch.rand(M, N, K, device=device, requires_grad=self.auto_set())
  
      def forward(self):
          return torch.ops.quantized.batch_norm1d(
              self.q_input_one, self.weight, self.bias, self.mean, self.var, self.eps,
              self.Y_scale, self.Y_zero_point)
  
  
  class QBatchNorm2dBenchmark(QBatchNormBenchmark):
      def _init(self, M, N, K, device):
          self.set_module_name("QBatchNorm2d")
          # Note: quantized implementation requires rank 4, which is why we
          # add a 1 as the last dimension
          self.input_one = torch.rand(M, N, K, 1, device=device, requires_grad=self.auto_set())
  
      def forward(self):
          return torch.ops.quantized.batch_norm2d(
              self.q_input_one, self.weight, self.bias, self.mean, self.var, self.eps,
              self.Y_scale, self.Y_zero_point)
  
  
  op_bench.generate_pt_test(batchnorm_configs_short, QBatchNorm1dBenchmark)
  op_bench.generate_pt_test(batchnorm_configs_short, QBatchNorm2dBenchmark)
  
  if __name__ == "__main__":
      op_bench.benchmark_runner.main()
   r"""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter
  
  To support these two classes, in `./_utils` we define many utility methods and
  functions to be run in multiprocessing. E.g., the data loading worker loop is
  in `./_utils/worker.py`.
  """
  
  import threading
  import itertools
  import warnings
  from typing import Any, Callable, TypeVar, Generic, Sequence, List, Optional
  
  import multiprocessing as python_multiprocessing
  import torch
  import torch.multiprocessing as multiprocessing
  from torch._utils import ExceptionWrapper
  from torch._six import queue, string_classes
  
  from . import IterableDataset, Sampler, SequentialSampler, RandomSampler, BatchSampler, Dataset
  from . import _utils
  
  T_co = TypeVar('T_co', covariant=True)
  T = TypeVar('T')
  _worker_init_fn_t = Callable[[int], None]
  
  # Ideally we would parameterize `DataLoader` by the return type of `collate_fn`, but there is currently no way to have that
  # type parameter set to a default value if the user doesn't pass in a custom 'collate_fn'.
  # See https://github.com/python/mypy/issues/3737.
  _collate_fn_t = Callable[[List[T]], Any]
  
  
  # This function used to be defined in this file. However, it was moved to
  # _utils/collate.py. Although it is rather hard to access this from user land
  # (one has to explicitly directly `import torch.utils.data.dataloader`), there
  # probably is user code out there using it. This aliasing maintains BC in this
  # aspect.
  default_collate: _collate_fn_t = _utils.collate.default_collate
  
  get_worker_info = _utils.worker.get_worker_info
  
  class _DatasetKind(object):
      Map = 0
      Iterable = 1
  
      @staticmethod
      def create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last):
          if kind == _DatasetKind.Map:
              return _utils.fetch._MapDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
          else:
              return _utils.fetch._IterableDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
  
  
  class _InfiniteConstantSampler(Sampler):
      r"""Analogous to ``itertools.repeat(None, None)``.
      Used as sampler for :class:`~torch.utils.data.IterableDataset`.
  
      Arguments:
          data_source (Dataset): dataset to sample from
      """
  
      def __init__(self):
          super(_InfiniteConstantSampler, self).__init__(None)
  
      def __iter__(self):
          while True:
              yield None
  
  
  class DataLoader(Generic[T_co]):
      r"""
      Data loader. Combines a dataset and a sampler, and provides an iterable over
      the given dataset.
  
      The :class:`~torch.utils.data.DataLoader` supports both map-style and
      iterable-style datasets with single- or multi-process loading, customizing
      loading order and optional automatic batching (collation) and memory pinning.
  
      See :py:mod:`torch.utils.data` documentation page for more details.
  
      Arguments:
          dataset (Dataset): dataset from which to load the data.
          batch_size (int, optional): how many samples per batch to load
              (default: ``1``).
          shuffle (bool, optional): set to ``True`` to have the data reshuffled
              at every epoch (default: ``False``).
          sampler (Sampler or Iterable, optional): defines the strategy to draw
              samples from the dataset. Can be any ``Iterable`` with ``__len__``
              implemented. If specified, :attr:`shuffle` must not be specified.
          batch_sampler (Sampler or Iterable, optional): like :attr:`sampler`, but
              returns a batch of indices at a time. Mutually exclusive with
              :attr:`batch_size`, :attr:`shuffle`, :attr:`sampler`,
              and :attr:`drop_last`.
          num_workers (int, optional): how many subprocesses to use for data
              loading. ``0`` means that the data will be loaded in the main process.
              (default: ``0``)
          collate_fn (callable, optional): merges a list of samples to form a
              mini-batch of Tensor(s).  Used when using batched loading from a
              map-style dataset.
          pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
              into CUDA pinned memory before returning them.  If your data elements
              are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
              see the example below.
          drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
              if the dataset size is not divisible by the batch size. If ``False`` and
              the size of dataset is not divisible by the batch size, then the last batch
              will be smaller. (default: ``False``)
          timeout (numeric, optional): if positive, the timeout value for collecting a batch
              from workers. Should always be non-negative. (default: ``0``)
          worker_init_fn (callable, optional): If not ``None``, this will be called on each
              worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
              input, after seeding and before data loading. (default: ``None``)
          prefetch_factor (int, optional, keyword-only arg): Number of sample loaded
              in advance by each worker. ``2`` means there will be a total of
              2 * num_workers samples prefetched across all workers. (default: ``2``)
          persistent_workers (bool, optional): If ``True``, the data loader will not shutdown
              the worker processes after a dataset has been consumed once. This allows to 
              maintain the workers `Dataset` instances alive. (default: ``False``)
  
  
      .. warning:: If the ``spawn`` start method is used, :attr:`worker_init_fn`
                   cannot be an unpicklable object, e.g., a lambda function. See
                   :ref:`multiprocessing-best-practices` on more details related
                   to multiprocessing in PyTorch.
  
      .. warning:: ``len(dataloader)`` heuristic is based on the length of the sampler used.
                   When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,
                   it instead returns an estimate based on ``len(dataset) / batch_size``, with proper
                   rounding depending on :attr:`drop_last`, regardless of multi-process loading
                   configurations. This represents the best guess PyTorch can make because PyTorch
                   trusts user :attr:`dataset` code in correctly handling multi-process
                   loading to avoid duplicate data.
  
                   However, if sharding results in multiple workers having incomplete last batches,
                   this estimate can still be inaccurate, because (1) an otherwise complete batch can
                   be broken into multiple ones and (2) more than one batch worth of samples can be
                   dropped when :attr:`drop_last` is set. Unfortunately, PyTorch can not detect such
                   cases in general.
  
                   See `Dataset Types`_ for more details on these two types of datasets and how
                   :class:`~torch.utils.data.IterableDataset` interacts with
                   `Multi-process data loading`_.
      """
      dataset: Dataset[T_co]
      batch_size: Optional[int]
      num_workers: int
      pin_memory: bool
      drop_last: bool
      timeout: float
      sampler: Sampler
      prefetch_factor: int
      _iterator : Optional['_BaseDataLoaderIter']
      __initialized = False
  
      def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1,
                   shuffle: bool = False, sampler: Optional[Sampler[int]] = None,
                   batch_sampler: Optional[Sampler[Sequence[int]]] = None,
                   num_workers: int = 0, collate_fn: _collate_fn_t = None,
                   pin_memory: bool = False, drop_last: bool = False,
                   timeout: float = 0, worker_init_fn: _worker_init_fn_t = None,
                   multiprocessing_context=None, generator=None,
                   *, prefetch_factor: int = 2,
                   persistent_workers: bool = False):
          torch._C._log_api_usage_once("python.data_loader")  # type: ignore
  
          if num_workers < 0:
              raise ValueError('num_workers option should be non-negative; '
                               'use num_workers=0 to disable multiprocessing.')
  
          if timeout < 0:
              raise ValueError('timeout option should be non-negative')
  
          if num_workers == 0 and prefetch_factor != 2:
              raise ValueError('prefetch_factor option could only be specified in multiprocessing.'
                               'let num_workers > 0 to enable multiprocessing.')
          assert prefetch_factor > 0
  
          if persistent_workers and num_workers == 0:
              raise ValueError('persistent_workers option needs num_workers > 0')
  
          self.dataset = dataset
          self.num_workers = num_workers
          self.prefetch_factor = prefetch_factor
          self.pin_memory = pin_memory
          self.timeout = timeout
          self.worker_init_fn = worker_init_fn
          self.multiprocessing_context = multiprocessing_context
  
          # Arg-check dataset related before checking samplers because we want to
          # tell users that iterable-style datasets are incompatible with custom
          # samplers first, so that they don't learn that this combo doesn't work
          # after spending time fixing the custom sampler errors.
          if isinstance(dataset, IterableDataset):
              self._dataset_kind = _DatasetKind.Iterable
              # NOTE [ Custom Samplers and IterableDataset ]
              #
              # `IterableDataset` does not support custom `batch_sampler` or
              # `sampler` since the key is irrelevant (unless we support
              # generator-style dataset one day...).
              #
              # For `sampler`, we always create a dummy sampler. This is an
              # infinite sampler even when the dataset may have an implemented
              # finite `__len__` because in multi-process data loading, naive
              # settings will return duplicated data (which may be desired), and
              # thus using a sampler with length matching that of dataset will
              # cause data lost (you may have duplicates of the first couple
              # batches, but never see anything afterwards). Therefore,
              # `Iterabledataset` always uses an infinite sampler, an instance of
              # `_InfiniteConstantSampler` defined above.
              #
              # A custom `batch_sampler` essentially only controls the batch size.
              # However, it is unclear how useful it would be since an iterable-style
              # dataset can handle that within itself. Moreover, it is pointless
              # in multi-process data loading as the assignment order of batches
              # to workers is an implementation detail so users can not control
              # how to batchify each worker's iterable. Thus, we disable this
              # option. If this turns out to be useful in future, we can re-enable
              # this, and support custom samplers that specify the assignments to
              # specific workers.
              if shuffle is not False:
                  raise ValueError(
                      "DataLoader with IterableDataset: expected unspecified "
                      "shuffle option, but got shuffle={}".format(shuffle))
              elif sampler is not None:
                  # See NOTE [ Custom Samplers and IterableDataset ]
                  raise ValueError(
                      "DataLoader with IterableDataset: expected unspecified "
                      "sampler option, but got sampler={}".format(sampler))
              elif batch_sampler is not None:
                  # See NOTE [ Custom Samplers and IterableDataset ]
                  raise ValueError(
                      "DataLoader with IterableDataset: expected unspecified "
                      "batch_sampler option, but got batch_sampler={}".format(batch_sampler))
          else:
              self._dataset_kind = _DatasetKind.Map
  
          if sampler is not None and shuffle:
              raise ValueError('sampler option is mutually exclusive with '
                               'shuffle')
  
          if batch_sampler is not None:
              # auto_collation with custom batch_sampler
              if batch_size != 1 or shuffle or sampler is not None or drop_last:
                  raise ValueError('batch_sampler option is mutually exclusive '
                                   'with batch_size, shuffle, sampler, and '
                                   'drop_last')
              batch_size = None
              drop_last = False
          elif batch_size is None:
              # no auto_collation
              if drop_last:
                  raise ValueError('batch_size=None option disables auto-batching '
                                   'and is mutually exclusive with drop_last')
  
          if sampler is None:  # give default samplers
              if self._dataset_kind == _DatasetKind.Iterable:
                  # See NOTE [ Custom Samplers and IterableDataset ]
                  sampler = _InfiniteConstantSampler()
              else:  # map-style
                  if shuffle:
                      # Cannot statically verify that dataset is Sized
                      # Somewhat related: see NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
                      sampler = RandomSampler(dataset, generator=generator)  # type: ignore
                  else:
                      sampler = SequentialSampler(dataset)
  
          if batch_size is not None and batch_sampler is None:
              # auto_collation without custom batch_sampler
              batch_sampler = BatchSampler(sampler, batch_size, drop_last)
  
          self.batch_size = batch_size
          self.drop_last = drop_last
          self.sampler = sampler
          self.batch_sampler = batch_sampler
          self.generator = generator
  
          if collate_fn is None:
              if self._auto_collation:
                  collate_fn = _utils.collate.default_collate
              else:
                  collate_fn = _utils.collate.default_convert
  
          self.collate_fn = collate_fn
          self.persistent_workers = persistent_workers
  
          self.__initialized = True
          self._IterableDataset_len_called = None  # See NOTE [ IterableDataset and __len__ ]
  
          self._iterator = None
  
      def _get_iterator(self) -> '_BaseDataLoaderIter':
          if self.num_workers == 0:
              return _SingleProcessDataLoaderIter(self)
          else:
              return _MultiProcessingDataLoaderIter(self)
  
      @property
      def multiprocessing_context(self):
          return self.__multiprocessing_context
  
      @multiprocessing_context.setter
      def multiprocessing_context(self, multiprocessing_context):
          if multiprocessing_context is not None:
              if self.num_workers > 0:
                  if not multiprocessing._supports_context:
                      raise ValueError('multiprocessing_context relies on Python >= 3.4, with '
                                       'support for different start methods')
  
                  if isinstance(multiprocessing_context, string_classes):
                      valid_start_methods = multiprocessing.get_all_start_methods()
                      if multiprocessing_context not in valid_start_methods:
                          raise ValueError(
                              ('multiprocessing_context option '
                               'should specify a valid start method in {!r}, but got '
                               'multiprocessing_context={!r}').format(valid_start_methods, multiprocessing_context))
                      # error: Argument 1 to "get_context" has incompatible type "Union[str, bytes]"; expected "str"  [arg-type]
                      multiprocessing_context = multiprocessing.get_context(multiprocessing_context)  # type: ignore
  
                  if not isinstance(multiprocessing_context, python_multiprocessing.context.BaseContext):
                      raise TypeError(('multiprocessing_context option should be a valid context '
                                       'object or a string specifying the start method, but got '
                                       'multiprocessing_context={}').format(multiprocessing_context))
              else:
                  raise ValueError(('multiprocessing_context can only be used with '
                                    'multi-process loading (num_workers > 0), but got '
                                    'num_workers={}').format(self.num_workers))
  
          self.__multiprocessing_context = multiprocessing_context
  
      def __setattr__(self, attr, val):
          if self.__initialized and attr in (
                  'batch_size', 'batch_sampler', 'sampler', 'drop_last', 'dataset', 'persistent_workers'):
              raise ValueError('{} attribute should not be set after {} is '
                               'initialized'.format(attr, self.__class__.__name__))
  
          super(DataLoader, self).__setattr__(attr, val)
  
      # We quote '_BaseDataLoaderIter' since it isn't defined yet and the definition can't be moved up
      # since '_BaseDataLoaderIter' references 'DataLoader'.
      def __iter__(self) -> '_BaseDataLoaderIter':
          # When using a single worker the returned iterator should be
          # created everytime to avoid reseting its state
          # However, in the case of a multiple workers iterator
          # the iterator is only created once in the lifetime of the
          # DataLoader object so that workers can be reused
          if self.persistent_workers and self.num_workers > 0:
              if self._iterator is None:
                  self._iterator = self._get_iterator()
              else:
                  self._iterator._reset(self)
              return self._iterator
          else:
              return self._get_iterator()
  
      @property
      def _auto_collation(self):
          return self.batch_sampler is not None
  
      @property
      def _index_sampler(self):
          # The actual sampler used for generating indices for `_DatasetFetcher`
          # (see _utils/fetch.py) to read data at each time. This would be
          # `.batch_sampler` if in auto-collation mode, and `.sampler` otherwise.
          # We can't change `.sampler` and `.batch_sampler` attributes for BC
          # reasons.
          if self._auto_collation:
              return self.batch_sampler
          else:
              return self.sampler
  
      def __len__(self) -> int:
          if self._dataset_kind == _DatasetKind.Iterable:
              # NOTE [ IterableDataset and __len__ ]
              #
              # For `IterableDataset`, `__len__` could be inaccurate when one naively
              # does multi-processing data loading, since the samples will be duplicated.
              # However, no real use case should be actually using that behavior, so
              # it should count as a user error. We should generally trust user
              # code to do the proper thing (e.g., configure each replica differently
              # in `__iter__`), and give us the correct `__len__` if they choose to
              # implement it (this will still throw if the dataset does not implement
              # a `__len__`).
              #
              # To provide a further warning, we track if `__len__` was called on the
              # `DataLoader`, save the returned value in `self._len_called`, and warn
              # if the iterator ends up yielding more than this number of samples.
  
              # Cannot statically verify that dataset is Sized
              length = self._IterableDataset_len_called = len(self.dataset)  # type: ignore
              if self.batch_size is not None:  # IterableDataset doesn't allow custom sampler or batch_sampler
                  from math import ceil
                  if self.drop_last:
                      length = length // self.batch_size
                  else:
                      length = ceil(length / self.batch_size)
              return length
          else:
              return len(self._index_sampler)
  
  
  class _BaseDataLoaderIter(object):
      def __init__(self, loader: DataLoader) -> None:
          self._dataset = loader.dataset
          self._dataset_kind = loader._dataset_kind
          self._IterableDataset_len_called = loader._IterableDataset_len_called
          self._auto_collation = loader._auto_collation
          self._drop_last = loader.drop_last
          self._index_sampler = loader._index_sampler
          self._num_workers = loader.num_workers
          self._prefetch_factor = loader.prefetch_factor
          self._pin_memory = loader.pin_memory and torch.cuda.is_available()
          self._timeout = loader.timeout
          self._collate_fn = loader.collate_fn
          self._sampler_iter = iter(self._index_sampler)
          self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
          self._persistent_workers = loader.persistent_workers
          self._num_yielded = 0
  
      def __iter__(self) -> '_BaseDataLoaderIter':
          return self
  
      def _reset(self, loader, first_iter=False):
          self._sampler_iter = iter(self._index_sampler)
          self._num_yielded = 0
          self._IterableDataset_len_called = loader._IterableDataset_len_called
  
      def _next_index(self):
          return next(self._sampler_iter)  # may raise StopIteration
  
      def _next_data(self):
          raise NotImplementedError
  
      def __next__(self) -> Any:
          if self._sampler_iter is None:
              self._reset()
          data = self._next_data()
          self._num_yielded += 1
          if self._dataset_kind == _DatasetKind.Iterable and \
                  self._IterableDataset_len_called is not None and \
                  self._num_yielded > self._IterableDataset_len_called:
              warn_msg = ("Length of IterableDataset {} was reported to be {} (when accessing len(dataloader)), but {} "
                          "samples have been fetched. ").format(self._dataset, self._IterableDataset_len_called,
                                                                self._num_yielded)
              if self._num_workers > 0:
                  warn_msg += ("For multiprocessing data-loading, this could be caused by not properly configuring the "
                               "IterableDataset replica at each worker. Please see "
                               "https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset for examples.")
              warnings.warn(warn_msg)
          return data
  
      next = __next__  # Python 2 compatibility
  
      def __len__(self) -> int:
          return len(self._index_sampler)
  
      def __getstate__(self):
          # TODO: add limited pickling support for sharing an iterator
          # across multiple threads for HOGWILD.
          # Probably the best way to do this is by moving the sample pushing
          # to a separate thread and then just sharing the data queue
          # but signalling the end is tricky without a non-blocking API
          raise NotImplementedError("{} cannot be pickled", self.__class__.__name__)
  
  
  class _SingleProcessDataLoaderIter(_BaseDataLoaderIter):
      def __init__(self, loader):
          super(_SingleProcessDataLoaderIter, self).__init__(loader)
          assert self._timeout == 0
          assert self._num_workers == 0
  
          self._dataset_fetcher = _DatasetKind.create_fetcher(
              self._dataset_kind, self._dataset, self._auto_collation, self._collate_fn, self._drop_last)
  
      def _next_data(self):
          index = self._next_index()  # may raise StopIteration
          data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
          if self._pin_memory:
              data = _utils.pin_memory.pin_memory(data)
          return data
  
  
  class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter):
      r"""Iterates once over the DataLoader's dataset, as specified by the sampler"""
  
      # NOTE [ Data Loader Multiprocessing Shutdown Logic ]
      #
      # Preliminary:
      #
      # Our data model looks like this (queues are indicated with curly brackets):
      #
      #                main process                              ||
      #                     |                                    ||
      #               {index_queue}                              ||
      #                     |                                    ||
      #              worker processes                            ||     DATA
      #                     |                                    ||
      #            {worker_result_queue}                         ||     FLOW
      #                     |                                    ||
      #      pin_memory_thread of main process                   ||   DIRECTION
      #                     |                                    ||
      #               {data_queue}                               ||
      #                     |                                    ||
      #                data output                               \/
      #
      # P.S. `worker_result_queue` and `pin_memory_thread` part may be omitted if
      #      `pin_memory=False`.
      #
      #
      # Terminating multiprocessing logic requires very careful design. In
      # particular, we need to make sure that
      #
      #   1. The iterator gracefully exits the workers when its last reference is
      #      gone or it is depleted.
      #
      #      In this case, the workers should be gracefully exited because the
      #      main process may still need to continue to run, and we want cleaning
      #      up code in the workers to be executed (e.g., releasing GPU memory).
      #      Naturally, we implement the shutdown logic in `__del__` of
      #      DataLoaderIterator.
      #
      #      We delay the discussion on the logic in this case until later.
      #
      #   2. The iterator exits the workers when the loader process and/or worker
      #      processes exits normally or with error.
      #
      #      We set all workers and `pin_memory_thread` to have `daemon=True`.
      #
      #      You may ask, why can't we make the workers non-daemonic, and
      #      gracefully exit using the same logic as we have in `__del__` when the
      #      iterator gets deleted (see 1 above)?
      #
      #      First of all, `__del__` is **not** guaranteed to be called when
      #      interpreter exits. Even if it is called, by the time it executes,
      #      many Python core library resources may alreay be freed, and even
      #      simple things like acquiring an internal lock of a queue may hang.
      #      Therefore, in this case, we actually need to prevent `__del__` from
      #      being executed, and rely on the automatic termination of daemonic
      #      children. Thus, we register an `atexit` hook that sets a global flag
      #      `_utils.python_exit_status`. Since `atexit` hooks are executed in the
      #      reverse order of registration, we are guaranteed that this flag is
      #      set before library resources we use are freed. (Hooks freeing those
      #      resources are registered at importing the Python core libraries at
      #      the top of this file.) So in `__del__`, we check if
      #      `_utils.python_exit_status` is set or `None` (freed), and perform
      #      no-op if so.
      #
      #      Another problem with `__del__` is also related to the library cleanup
      #      calls. When a process ends, it shuts the all its daemonic children
      #      down with a SIGTERM (instead of joining them without a timeout).
      #      Simiarly for threads, but by a different mechanism. This fact,
      #      together with a few implementation details of multiprocessing, forces
      #      us to make workers daemonic. All of our problems arise when a
      #      DataLoader is used in a subprocess, and are caused by multiprocessing
      #      code which looks more or less like this:
      #
      #          try:
      #              your_function_using_a_dataloader()
      #          finally:
      #              multiprocessing.util._exit_function()
      #
      #      The joining/termination mentioned above happens inside
      #      `_exit_function()`. Now, if `your_function_using_a_dataloader()`
      #      throws, the stack trace stored in the exception will prevent the
      #      frame which uses `DataLoaderIter` to be freed. If the frame has any
      #      reference to the `DataLoaderIter` (e.g., in a method of the iter),
      #      its  `__del__`, which starts the shutdown procedure, will not be
      #      called. That, in turn, means that workers aren't notified. Attempting
      #      to join in `_exit_function` will then result in a hang.
      #
      #      For context, `_exit_function` is also registered as an `atexit` call.
      #      So it is unclear to me (@ssnl) why this is needed in a finally block.
      #      The code dates back to 2008 and there is no comment on the original
      #      PEP 371 or patch https://bugs.python.org/issue3050 (containing both
      #      the finally block and the `atexit` registration) that explains this.
      #
      #      Another choice is to just shutdown workers with logic in 1 above
      #      whenever we see an error in `next`. This isn't ideal because
      #        a. It prevents users from using try-catch to resume data loading.
      #        b. It doesn't prevent hanging if users have references to the
      #           iterator.
      #
      #   3. All processes exit if any of them die unexpectedly by fatal signals.
      #
      #      As shown above, the workers are set as daemonic children of the main
      #      process. However, automatic cleaning-up of such child processes only
      #      happens if the parent process exits gracefully (e.g., not via fatal
      #      signals like SIGKILL). So we must ensure that each process will exit
      #      even the process that should send/receive data to/from it were
      #      killed, i.e.,
      #
      #        a. A process won't hang when getting from a queue.
      #
      #           Even with carefully designed data dependencies (i.e., a `put()`
      #           always corresponding to a `get()`), hanging on `get()` can still
      #           happen when data in queue is corrupted (e.g., due to
      #           `cancel_join_thread` or unexpected exit).
      #
      #           For child exit, we set a timeout whenever we try to get data
      #           from `data_queue`, and check the workers' status on each timeout
      #           and error.
      #           See `_DataLoaderiter._get_batch()` and
      #           `_DataLoaderiter._try_get_data()` for details.
      #
      #           Additionally, for child exit on non-Windows platforms, we also
      #           register a SIGCHLD handler (which is supported on Windows) on
      #           the main process, which checks if any of the workers fail in the
      #           (Python) handler. This is more efficient and faster in detecting
      #           worker failures, compared to only using the above mechanism.
      #           See `DataLoader.cpp` and `_utils/signal_handling.py` for details.
      #
      #           For `.get()` calls where the sender(s) is not the workers, we
      #           guard them with timeouts, and check the status of the sender
      #           when timeout happens:
      #             + in the workers, the `_utils.worker.ManagerWatchdog` class
      #               checks the status of the main process.
      #             + if `pin_memory=True`, when getting from `pin_memory_thread`,
      #               check `pin_memory_thread` status periodically until `.get()`
      #               returns or see that `pin_memory_thread` died.
      #
      #        b. A process won't hang when putting into a queue;
      #
      #           We use `mp.Queue` which has a separate background thread to put
      #           objects from an unbounded buffer array. The background thread is
      #           daemonic and usually automatically joined when the process
      #           exits.
      #
      #           However, in case that the receiver has ended abruptly while
      #           reading from the pipe, the join will hang forever. Therefore,
      #           for both `worker_result_queue` (worker -> main process/pin_memory_thread)
      #           and each `index_queue` (main process -> worker), we use
      #           `q.cancel_join_thread()` in sender process before any `q.put` to
      #           prevent this automatic join.
      #
      #           Moreover, having all queues called `cancel_join_thread` makes
      #           implementing graceful shutdown logic in `__del__` much easier.
      #           It won't need to get from any queue, which would also need to be
      #           guarded by periodic status checks.
      #
      #           Nonetheless, `cancel_join_thread` must only be called when the
      #           queue is **not** going to be read from or write into by another
      #           process, because it may hold onto a lock or leave corrupted data
      #           in the queue, leading other readers/writers to hang.
      #
      #           `pin_memory_thread`'s `data_queue` is a `queue.Queue` that does
      #           a blocking `put` if the queue is full. So there is no above
      #           problem, but we do need to wrap the `put` in a loop that breaks
      #           not only upon success, but also when the main process stops
      #           reading, i.e., is shutting down.
      #
      #
      # Now let's get back to 1:
      #   how we gracefully exit the workers when the last reference to the
      #   iterator is gone.
      #
      # To achieve this, we implement the following logic along with the design
      # choices mentioned above:
      #
      # `workers_done_event`:
      #   A `multiprocessing.Event` shared among the main process and all worker
      #   processes. This is used to signal the workers that the iterator is
      #   shutting down. After it is set, they will not send processed data to
      #   queues anymore, and only wait for the final `None` before exiting.
      #   `done_event` isn't strictly needed. I.e., we can just check for `None`
      #   from the input queue, but it allows us to skip wasting resources
      #   processing data if we are already shutting down.
      #
      # `pin_memory_thread_done_event`:
      #   A `threading.Event` for a similar purpose to that of
      #   `workers_done_event`, but is for the `pin_memory_thread`. The reason
      #   that separate events are needed is that `pin_memory_thread` reads from
      #   the output queue of the workers. But the workers, upon seeing that
      #   `workers_done_event` is set, only wants to see the final `None`, and is
      #   not required to flush all data in the output queue (e.g., it may call
      #   `cancel_join_thread` on that queue if its `IterableDataset` iterator
      #   happens to exhaust coincidentally, which is out of the control of the
      #   main process). Thus, since we will exit `pin_memory_thread` before the
      #   workers (see below), two separete events are used.
      #
      # NOTE: In short, the protocol is that the main process will set these
      #       `done_event`s and then the corresponding processes/threads a `None`,
      #       and that they may exit at any time after receiving the `None`.
      #
      # NOTE: Using `None` as the final signal is valid, since normal data will
      #       always be a 2-tuple with the 1st element being the index of the data
      #       transferred (different from dataset index/key), and the 2nd being
      #       either the dataset key or the data sample (depending on which part
      #       of the data model the queue is at).
      #
      # [ worker processes ]
      #   While loader process is alive:
      #     Get from `index_queue`.
      #       If get anything else,
      #          Check `workers_done_event`.
      #            If set, continue to next iteration
      #                    i.e., keep getting until see the `None`, then exit.
      #            Otherwise, process data:
      #                If is fetching from an `IterableDataset` and the iterator
      #                    is exhausted, send an `_IterableDatasetStopIteration`
      #                    object to signal iteration end. The main process, upon
      #                    receiving such an object, will send `None` to this
      #                    worker and not use the corresponding `index_queue`
      #                    anymore.
      #       If timed out,
      #          No matter `workers_done_event` is set (still need to see `None`)
      #          or not, must continue to next iteration.
      #   (outside loop)
      #   If `workers_done_event` is set,  (this can be False with `IterableDataset`)
      #     `data_queue.cancel_join_thread()`.  (Everything is ending here:
      #                                          main process won't read from it;
      #                                          other workers will also call
      #                                          `cancel_join_thread`.)
      #
      # [ pin_memory_thread ]
      #   # No need to check main thread. If this thread is alive, the main loader
      #   # thread must be alive, because this thread is set as daemonic.
      #   While `pin_memory_thread_done_event` is not set:
      #     Get from `index_queue`.
      #       If timed out, continue to get in the next iteration.
      #       Otherwise, process data.
      #       While `pin_memory_thread_done_event` is not set:
      #         Put processed data to `data_queue` (a `queue.Queue` with blocking put)
      #         If timed out, continue to put in the next iteration.
      #         Otherwise, break, i.e., continuing to the out loop.
      #
      #   NOTE: we don't check the status of the main thread because
      #           1. if the process is killed by fatal signal, `pin_memory_thread`
      #              ends.
      #           2. in other cases, either the cleaning-up in __del__ or the
      #              automatic exit of daemonic thread will take care of it.
      #              This won't busy-wait either because `.get(timeout)` does not
      #              busy-wait.
      #
      # [ main process ]
      #   In the DataLoader Iter's `__del__`
      #     b. Exit `pin_memory_thread`
      #          i.   Set `pin_memory_thread_done_event`.
      #          ii   Put `None` in `worker_result_queue`.
      #          iii. Join the `pin_memory_thread`.
      #          iv.  `worker_result_queue.cancel_join_thread()`.
      #
      #     c. Exit the workers.
      #          i.   Set `workers_done_event`.
      #          ii.  Put `None` in each worker's `index_queue`.
      #          iii. Join the workers.
      #          iv.  Call `.cancel_join_thread()` on each worker's `index_queue`.
      #
      #        NOTE: (c) is better placed after (b) because it may leave corrupted
      #              data in `worker_result_queue`, which `pin_memory_thread`
      #              reads from, in which case the `pin_memory_thread` can only
      #              happen at timeing out, which is slow. Nonetheless, same thing
      #              happens if a worker is killed by signal at unfortunate times,
      #              but in other cases, we are better off having a non-corrupted
      #              `worker_result_queue` for `pin_memory_thread`.
      #
      #   NOTE: If `pin_memory=False`, there is no `pin_memory_thread` and (b)
      #         can be omitted
      #
      # NB: `done_event`s isn't strictly needed. E.g., we can just check for
      #     `None` from `index_queue`, but it allows us to skip wasting resources
      #     processing indices already in `index_queue` if we are already shutting
      #     down.
  
      def __init__(self, loader):
          super(_MultiProcessingDataLoaderIter, self).__init__(loader)
  
          assert self._num_workers > 0
          assert self._prefetch_factor > 0
  
          if loader.multiprocessing_context is None:
              multiprocessing_context = multiprocessing
          else:
              multiprocessing_context = loader.multiprocessing_context
  
          self._worker_init_fn = loader.worker_init_fn
          self._worker_queue_idx_cycle = itertools.cycle(range(self._num_workers))
          # No certainty which module multiprocessing_context is
          self._worker_result_queue = multiprocessing_context.Queue()  # type: ignore
          self._worker_pids_set = False
          self._shutdown = False
          self._workers_done_event = multiprocessing_context.Event()
  
          self._index_queues = []
          self._workers = []
          for i in range(self._num_workers):
              # No certainty which module multiprocessing_context is
              index_queue = multiprocessing_context.Queue()  # type: ignore
              # index_queue.cancel_join_thread()
              w = multiprocessing_context.Process(
                  target=_utils.worker._worker_loop,
                  args=(self._dataset_kind, self._dataset, index_queue,
                        self._worker_result_queue, self._workers_done_event,
                        self._auto_collation, self._collate_fn, self._drop_last,
                        self._base_seed + i, self._worker_init_fn, i, self._num_workers,
                        self._persistent_workers))
              w.daemon = True
              # NB: Process.start() actually take some time as it needs to
              #     start a process and pass the arguments over via a pipe.
              #     Therefore, we only add a worker to self._workers list after
              #     it started, so that we do not call .join() if program dies
              #     before it starts, and __del__ tries to join but will get:
              #     AssertionError: can only join a started process.
              w.start()
              self._index_queues.append(index_queue)
              self._workers.append(w)
  
          if self._pin_memory:
              self._pin_memory_thread_done_event = threading.Event()
  
              # Queue is not type-annotated
              self._data_queue = queue.Queue()  # type: ignore
              pin_memory_thread = threading.Thread(
                  target=_utils.pin_memory._pin_memory_loop,
                  args=(self._worker_result_queue, self._data_queue,
                        torch.cuda.current_device(),
                        self._pin_memory_thread_done_event))
              pin_memory_thread.daemon = True
              pin_memory_thread.start()
              # Similar to workers (see comment above), we only register
              # pin_memory_thread once it is started.
              self._pin_memory_thread = pin_memory_thread
          else:
              self._data_queue = self._worker_result_queue
  
          # .pid can be None only before process is spawned (not the case, so ignore)
          _utils.signal_handling._set_worker_pids(id(self), tuple(w.pid for w in self._workers))  # type: ignore
          _utils.signal_handling._set_SIGCHLD_handler()
          self._worker_pids_set = True
          self._reset(loader, first_iter=True)
  
      def _reset(self, loader, first_iter=False):
          super()._reset(loader, first_iter)
          self._send_idx = 0  # idx of the next task to be sent to workers
          self._rcvd_idx = 0  # idx of the next task to be returned in __next__
          # information about data not yet yielded, i.e., tasks w/ indices in range [rcvd_idx, send_idx).
          # map: task idx => - (worker_id,)        if data isn't fetched (outstanding)
          #                  \ (worker_id, data)   if data is already fetched (out-of-order)
          self._task_info = {}
          self._tasks_outstanding = 0  # always equal to count(v for v in task_info.values() if len(v) == 1)
          # A list of booleans representing whether each worker still has work to
          # do, i.e., not having exhausted its iterable dataset object. It always
          # contains all `True`s if not using an iterable-style dataset
          # (i.e., if kind != Iterable).
          # Not that this indicates that a worker still has work to do *for this epoch*.
          # It does not mean that a worker is dead. In case of `_persistent_workers`, 
          # the worker will be reset to available in the next epoch.
          self._workers_status = [True for i in range(self._num_workers)]
          # We resume the prefetching in case it was enabled
          if not first_iter:
              for idx in range(self._num_workers):
                  self._index_queues[idx].put(_utils.worker._ResumeIteration())
              resume_iteration_cnt = self._num_workers
              while resume_iteration_cnt > 0:
                  data = self._get_data()
                  if isinstance(data, _utils.worker._ResumeIteration):
                      resume_iteration_cnt -= 1
          # prime the prefetch loop
          for _ in range(self._prefetch_factor * self._num_workers):
              self._try_put_index()
  
      def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):
          # Tries to fetch data from `self._data_queue` once for a given timeout.
          # This can also be used as inner loop of fetching without timeout, with
          # the sender status as the loop condition.
          #
          # This raises a `RuntimeError` if any worker died expectedly. This error
          # can come from either the SIGCHLD handler in `_utils/signal_handling.py`
          # (only for non-Windows platforms), or the manual check below on errors
          # and timeouts.
          #
          # Returns a 2-tuple:
          #   (bool: whether successfully get data, any: data if successful else None)
          try:
              data = self._data_queue.get(timeout=timeout)
              return (True, data)
          except Exception as e:
              # At timeout and error, we manually check whether any worker has
              # failed. Note that this is the only mechanism for Windows to detect
              # worker failures.
              failed_workers = []
              for worker_id, w in enumerate(self._workers):
                  if self._workers_status[worker_id] and not w.is_alive():
                      failed_workers.append(w)
                      self._mark_worker_as_unavailable(worker_id)
              if len(failed_workers) > 0:
                  pids_str = ', '.join(str(w.pid) for w in failed_workers)
                  raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
              if isinstance(e, queue.Empty):
                  return (False, None)
              import tempfile
              import errno
              try:
                  # Raise an exception if we are this close to the FDs limit.
                  # Apparently, trying to open only one file is not a sufficient
                  # test.
                  # See NOTE [ DataLoader on Linux and open files limit ]
                  fds_limit_margin = 10
                  fs = [tempfile.NamedTemporaryFile() for i in range(fds_limit_margin)]
              except OSError as e:
                  if e.errno == errno.EMFILE:
                      raise RuntimeError(
                          "Too many open files. Communication with the"
                          " workers is no longer possible. Please increase the"
                          " limit using `ulimit -n` in the shell or change the"
                          " sharing strategy by calling"
                          " `torch.multiprocessing.set_sharing_strategy('file_system')`"
                          " at the beginning of your code") from None
              raise
  
  # NOTE [ DataLoader on Linux and open files limit ]
  #
  # On Linux when DataLoader is used with multiprocessing we pass the data between
  # the root process and the workers through SHM files. We remove those files from
  # the filesystem as soon as they are created and keep them alive by
  # passing around their file descriptors through AF_UNIX sockets. (See
  # docs/source/multiprocessing.rst and 'Multiprocessing Technical Notes` in
  # the wiki (https://github.com/pytorch/pytorch/wiki).)
  #
  # This sometimes leads us to exceeding the open files limit. When that happens,
  # and the offending file descriptor is coming over a socket, the `socket` Python
  # package silently strips the file descriptor from the message, setting only the
  # `MSG_CTRUNC` flag (which might be a bit misleading since the manpage says that
  # it _indicates that some control data were discarded due to lack of space in
  # the buffer for ancillary data_). This might reflect the C implementation of
  # AF_UNIX sockets.
  #
  # This behaviour can be reproduced with the script and instructions at the
  # bottom of this note.
  #
  # When that happens, the standard Python `multiprocessing` (and not
  # `torch.multiprocessing`) raises a `RuntimeError: received 0 items of ancdata`
  #
  # Sometimes, instead of the FD being stripped, you may get an `OSError:
  # Too many open files`, both in the script below and in DataLoader. However,
  # this is rare and seems to be nondeterministic.
  #
  #
  #   #!/usr/bin/env python3
  #   import sys
  #   import socket
  #   import os
  #   import array
  #   import shutil
  #   import socket
  #
  #
  #   if len(sys.argv) != 4:
  #       print("Usage: ", sys.argv[0], " tmp_dirname iteration (send|recv)")
  #       sys.exit(1)
  #
  #   if __name__ == '__main__':
  #       dirname = sys.argv[1]
  #       sock_path = dirname + "/sock"
  #       iterations = int(sys.argv[2])
  #       def dummy_path(i):
  #           return dirname + "/" + str(i) + ".dummy"
  #
  #
  #       if sys.argv[3] == 'send':
  #           while not os.path.exists(sock_path):
  #               pass
  #           client = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
  #           client.connect(sock_path)
  #           for i in range(iterations):
  #               fd = os.open(dummy_path(i), os.O_WRONLY | os.O_CREAT)
  #               ancdata = array.array('i', [fd])
  #               msg = bytes([i % 256])
  #               print("Sending fd ", fd, " (iteration #", i, ")")
  #               client.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, ancdata)])
  #
  #
  #       else:
  #           assert sys.argv[3] == 'recv'
  #
  #           if os.path.exists(dirname):
  #               raise Exception("Directory exists")
  #
  #           os.mkdir(dirname)
  #
  #           print("Opening socket...")
  #           server = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
  #           server.bind(sock_path)
  #
  #           print("Listening...")
  #           for i in range(iterations):
  #               a = array.array('i')
  #               msg, ancdata, flags, addr = server.recvmsg(1, socket.CMSG_SPACE(a.itemsize))
  #               assert(len(ancdata) == 1)
  #               cmsg_level, cmsg_type, cmsg_data = ancdata[0]
  #               a.frombytes(cmsg_data)
  #               print("Received fd ", a[0], " (iteration #", i, ")")
  #
  #           shutil.rmtree(dirname)
  #
  # Steps to reproduce:
  #
  # 1. Run two shells and set lower file descriptor limit in the receiving one:
  # (shell1) ulimit -n 1020
  # (shell2) ulimit -n 1022
  #
  # 2. Run the script above with the `recv` option in the first shell
  # (shell1) ./test_socket.py sock_tmp 1017 recv
  #
  # 3. Run the script with the `send` option in the second shell:
  # (shell2) ./test_socket.py sock_tmp 1017 send
  
      def _get_data(self):
          # Fetches data from `self._data_queue`.
          #
          # We check workers' status every `MP_STATUS_CHECK_INTERVAL` seconds,
          # which we achieve by running `self._try_get_data(timeout=MP_STATUS_CHECK_INTERVAL)`
          # in a loop. This is the only mechanism to detect worker failures for
          # Windows. For other platforms, a SIGCHLD handler is also used for
          # worker failure detection.
          #
          # If `pin_memory=True`, we also need check if `pin_memory_thread` had
          # died at timeouts.
          if self._timeout > 0:
              success, data = self._try_get_data(self._timeout)
              if success:
                  return data
              else:
                  raise RuntimeError('DataLoader timed out after {} seconds'.format(self._timeout))
          elif self._pin_memory:
              while self._pin_memory_thread.is_alive():
                  success, data = self._try_get_data()
                  if success:
                      return data
              else:
                  # while condition is false, i.e., pin_memory_thread died.
                  raise RuntimeError('Pin memory thread exited unexpectedly')
              # In this case, `self._data_queue` is a `queue.Queue`,. But we don't
              # need to call `.task_done()` because we don't use `.join()`.
          else:
              while True:
                  success, data = self._try_get_data()
                  if success:
                      return data
  
      def _next_data(self):
          while True:
              # If the worker responsible for `self._rcvd_idx` has already ended
              # and was unable to fulfill this task (due to exhausting an `IterableDataset`),
              # we try to advance `self._rcvd_idx` to find the next valid index.
              #
              # This part needs to run in the loop because both the `self._get_data()`
              # call and `_IterableDatasetStopIteration` check below can mark
              # extra worker(s) as dead.
              while self._rcvd_idx < self._send_idx:
                  info = self._task_info[self._rcvd_idx]
                  worker_id = info[0]
                  if len(info) == 2 or self._workers_status[worker_id]:  # has data or is still active
                      break
                  del self._task_info[self._rcvd_idx]
                  self._rcvd_idx += 1
              else:
                  # no valid `self._rcvd_idx` is found (i.e., didn't break)
                  if not self._persistent_workers:
                      self._shutdown_workers()
                  raise StopIteration
  
              # Now `self._rcvd_idx` is the batch index we want to fetch
  
              # Check if the next sample has already been generated
              if len(self._task_info[self._rcvd_idx]) == 2:
                  data = self._task_info.pop(self._rcvd_idx)[1]
                  return self._process_data(data)
  
              assert not self._shutdown and self._tasks_outstanding > 0
              idx, data = self._get_data()
              self._tasks_outstanding -= 1
              if self._dataset_kind == _DatasetKind.Iterable:
                  # Check for _IterableDatasetStopIteration
                  if isinstance(data, _utils.worker._IterableDatasetStopIteration):
                      if self._persistent_workers:
                          self._workers_status[data.worker_id] = False
                      else:
                          self._mark_worker_as_unavailable(data.worker_id)
                      self._try_put_index()
                      continue
  
              if idx != self._rcvd_idx:
                  # store out-of-order samples
                  self._task_info[idx] += (data,)
              else:
                  del self._task_info[idx]
                  return self._process_data(data)
  
      def _try_put_index(self):
          assert self._tasks_outstanding < self._prefetch_factor * self._num_workers
  
          try:
              index = self._next_index()
          except StopIteration:
              return
          for _ in range(self._num_workers):  # find the next active worker, if any
              worker_queue_idx = next(self._worker_queue_idx_cycle)
              if self._workers_status[worker_queue_idx]:
                  break
          else:
              # not found (i.e., didn't break)
              return
  
          self._index_queues[worker_queue_idx].put((self._send_idx, index))
          self._task_info[self._send_idx] = (worker_queue_idx,)
          self._tasks_outstanding += 1
          self._send_idx += 1
  
      def _process_data(self, data):
          self._rcvd_idx += 1
          self._try_put_index()
          if isinstance(data, ExceptionWrapper):
              data.reraise()
          return data
  
      def _mark_worker_as_unavailable(self, worker_id, shutdown=False):
          # Mark a worker as having finished its work e.g., due to
          # exhausting an `IterableDataset`. This should be used only when this
          # `_MultiProcessingDataLoaderIter` is going to continue running.
  
          assert self._workers_status[worker_id] or (self._persistent_workers and shutdown)
  
          # Signal termination to that specific worker.
          q = self._index_queues[worker_id]
          # Indicate that no more data will be put on this queue by the current
          # process.
          q.put(None)
  
          # Note that we don't actually join the worker here, nor do we remove the
          # worker's pid from C side struct because (1) joining may be slow, and
          # (2) since we don't join, the worker may still raise error, and we
          # prefer capturing those, rather than ignoring them, even though they
          # are raised after the worker has finished its job.
          # Joinning is deferred to `_shutdown_workers`, which it is called when
          # all workers finish their jobs (e.g., `IterableDataset` replicas) or
          # when this iterator is garbage collected.
  
          self._workers_status[worker_id] = False
  
          assert self._workers_done_event.is_set() == shutdown
  
      def _shutdown_workers(self):
          # Called when shutting down this `_MultiProcessingDataLoaderIter`.
          # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on
          # the logic of this function.
          python_exit_status = _utils.python_exit_status
          if python_exit_status is True or python_exit_status is None:
              # See (2) of the note. If Python is shutting down, do no-op.
              return
          # Normal exit when last reference is gone / iterator is depleted.
          # See (1) and the second half of the note.
          if not self._shutdown:
              self._shutdown = True
              try:
                  # Exit `pin_memory_thread` first because exiting workers may leave
                  # corrupted data in `worker_result_queue` which `pin_memory_thread`
                  # reads from.
                  if hasattr(self, '_pin_memory_thread'):
                      # Use hasattr in case error happens before we set the attribute.
                      self._pin_memory_thread_done_event.set()
                      # Send something to pin_memory_thread in case it is waiting
                      # so that it can wake up and check `pin_memory_thread_done_event`
                      self._worker_result_queue.put((None, None))
                      self._pin_memory_thread.join()
                      self._worker_result_queue.cancel_join_thread()
                      self._worker_result_queue.close()
  
                  # Exit workers now.
                  self._workers_done_event.set()
                  for worker_id in range(len(self._workers)):
                      # Get number of workers from `len(self._workers)` instead of
                      # `self._num_workers` in case we error before starting all
                      # workers.
                      # If we are using workers_status with persistent_workers
                      # we have to shut it down because the worker is paused
                      if self._persistent_workers or self._workers_status[worker_id]:
                          self._mark_worker_as_unavailable(worker_id, shutdown=True)
                  for w in self._workers:
                      w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
                      if w.is_alive():
                          # Existing mechanisms try to make the workers exit
                          # peacefully, but in case that we unfortunately reach
                          # here, which we shouldn't, (e.g., pytorch/pytorch#39570),
                          # we kill the worker.
                          w.terminate()
                  for q in self._index_queues:
                      q.cancel_join_thread()
                      q.close()
              finally:
                  # Even though all this function does is putting into queues that
                  # we have called `cancel_join_thread` on, weird things can
                  # happen when a worker is killed by a signal, e.g., hanging in
                  # `Event.set()`. So we need to guard this with SIGCHLD handler,
                  # and remove pids from the C side data structure only at the
                  # end.
                  #
                  # FIXME: Unfortunately, for Windows, we are missing a worker
                  #        error detection mechanism here in this function, as it
                  #        doesn't provide a SIGCHLD handler.
                  if self._worker_pids_set:
                      _utils.signal_handling._remove_worker_pids(id(self))
                      self._worker_pids_set = False
  
      def __del__(self):
          self._shutdown_workers()
  
apis: torch.rand torch.ops.quantized.batch_norm2d torch.quantize_per_tensor torch.ops.quantized.batch_norm1d
apis: torch.empty torch.multiprocessing.get_all_start_methods torch.cuda.current_device torch._six.queue.Queue torch.multiprocessing.get_context torch.cuda.is_available torch._C._log_api_usage_once