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.
class_docstr_tok: ["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","."]
docstr: Return the data configuration.
code: def get_data_config(self) -> Dict[str, Any]:
      """
      Return the data configuration.
      """
      return self.get_experiment_config().get("data", {})
  
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 37 for calling the PyTorchTrialContext obj's underlying member method code with example usage: obj.get_data_config() and returns: self