class_docstr: Contains runtime information for any Determined workflow that uses the ``PyTorch`` API.
  
  With this class, users can do the following things:
  
  1. Wrap PyTorch models, optimizers, and LR schedulers with their Determined-compatible
     counterparts using :meth:`wrap_model`, :meth:`wrap_optimizer`, :meth:`wrap_lr_scheduler`,
     respectively. The Determined-compatible objects are capable of transparent
     distributed training, checkpointing and exporting, mixed-precision training,
     and gradient aggregation.
  2. Configure apex amp by calling :meth:`configure_apex_amp` (optional).
  3. Calculate the gradients with :meth:`backward` on a specified loss.
  4. Run an optimization step with :meth:`step_optimizer`.
  5. Functionalities inherited from :class:`determined.TrialContext`, including getting
     the runtime information and properly handling training data in distributed training.
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docstr: Map generated data to the device allocated by the Determined cluster.
  
  All the data in the data loader and the models are automatically moved to the
  allocated device. This method aims at providing a function for the data generated
  on the fly.
code: def to_device(self, data: pytorch._Data) -> pytorch.TorchData:
      """Map generated data to the device allocated by the Determined cluster.
  
      All the data in the data loader and the models are automatically moved to the
      allocated device. This method aims at providing a function for the data generated
      on the fly.
      """
      with self._record_timing("to_device", accumulate=True):
          return pytorch.to_device(
              data, self.device, self._to_device_warned_types
          )
  
imports: import contextlib
  import logging
  import pathlib
  import warnings
  from typing import Any, Callable, Dict, Iterator, List, Optional, Set, Tuple, Type, Union
  import torch
  from torch import nn
  import determined
  from determined import profiler, pytorch, util
  from determined.horovod import hvd
label_desc: Use image node_id 23 for calling the PyTorchTrialContext obj's underlying member method code with example usage: obj.to_device(data) and returns: pytorch