docstr: ``disable_dataset_reproducibility_checks()`` allows you to return an arbitrary
  ``DataLoader`` from :meth:`~determined.pytorch.PyTorchTrial.build_training_data_loader` or
  :meth:`~determined.pytorch.PyTorchTrial.build_validation_data_loader`.
  
  Normally you would be required to return a ``det.pytorch.DataLoader`` instead, which would
  guarantee that an appropriate ``Sampler`` is used that ensures:
  
  - When ``shuffle=True``, the shuffle is reproducible.
  - The dataset will start at the right location, even after pausing/continuing.
  - Proper sharding is used during distributed training.
  
  However, there may be cases where either reproducibility of the dataset is not needed or
  where the nature of the dataset may cause the ``det.pytorch.DataLoader`` to be unsuitable.
  
  In those cases, you may call ``disable_dataset_reproducibility_checks()`` and you will be
  free to return any ``torch.utils.data.DataLoader`` you like.  Dataset reproducibility will
  still be possible, but it will be your responsibility.  If desired, you may find the
  ``Sampler`` classes in :mod:`determined.pytorch.samplers` to be helpful.
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code: def disable_dataset_reproducibility_checks(self) -> None:
      """
      ``disable_dataset_reproducibility_checks()`` allows you to return an arbitrary
      ``DataLoader`` from :meth:`~determined.pytorch.PyTorchTrial.build_training_data_loader` or
      :meth:`~determined.pytorch.PyTorchTrial.build_validation_data_loader`.
  
      Normally you would be required to return a ``det.pytorch.DataLoader`` instead, which would
      guarantee that an appropriate ``Sampler`` is used that ensures:
  
      - When ``shuffle=True``, the shuffle is reproducible.
      - The dataset will start at the right location, even after pausing/continuing.
      - Proper sharding is used during distributed training.
  
      However, there may be cases where either reproducibility of the dataset is not needed or
      where the nature of the dataset may cause the ``det.pytorch.DataLoader`` to be unsuitable.
  
      In those cases, you may call ``disable_dataset_reproducibility_checks()`` and you will be
      free to return any ``torch.utils.data.DataLoader`` you like.  Dataset reproducibility will
      still be possible, but it will be your responsibility.  If desired, you may find the
      ``Sampler`` classes in :mod:`determined.pytorch.samplers` to be helpful.
      """
  
      self._data_repro_checks_disabled = True
      logger.info("disabled dataset reproducibility checks")
  
imports: import logging
  from typing import Any
label_desc: Use image node_id 3 for calling the PyTorchExperimentalContext obj's underlying member method code with example usage: obj.disable_dataset_reproducibility_checks() without return types