class_docstr: CheckpointLoadContext is a special PyTorchTrialContext that can be used to load Trial classes
  outside of normal training loops.
  
  It does not support actually reporting metrics to a real master or uploading checkpoints or any
  of the normal behaviors associated with model training.  :func:`determined.pytorch.init()`
  should still be used for normal training.
  
  CheckpointLoadContext is meant to be used by users using the PyTorchTrial Trainer directly.
  Users using the Trainer might prefer CheckpointLoadContext because it allows them to create a
  Trial class with extra parameters they may have added.
  
  Users who are relying on the legacy `entrypoint: my_model:MyTrainer` way of launching their code
  should continue to use :func:`~determined.pytorch.load_trial_from_checkpoint_path()`.
  Example usage:
  
  .. code:: python
  
     import determined as det
     from determined import pytorch
     from determined.experimental import client
  
     # Download checkpoint and load training code from checkpoint.
     path = client.get_checkpoint(MY_UUID)
     with det.import_from_path(path + "/code/"):
         import my_model_def
  
     # Create CheckpointLoadContext for instantiating trial.
     context = pytorch.CheckpointLoadContext()
  
     # Instantiate trial with context and any other args.
     my_trial = my_model_def.MyTrial(context, ...)
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code: def __init__(
      self,
      hparams: Optional[Dict] = None,
      exp_conf: Optional[Dict[str, Any]] = None,
  ) -> None:
      _, container_gpus, _ = det._get_gpus(limit_gpus=False)
      super().__init__(
          core_context=core._dummy_init(),
          trial_seed=0,
          hparams=hparams,
          slots_per_trial=1,
          num_gpus=len(container_gpus),
          exp_conf=exp_conf,
          aggregation_frequency=1,
          steps_completed=0,
          managed_training=False,
          debug_enabled=False,
      )
  
file_path: determined/harness/determined/pytorch/_load.py
imports: import json
  import logging
  import pathlib
  import warnings
  from typing import Any, Dict, Optional, Tuple, Type, cast
  import torch
  import determined
  from determined import core, errors, load, pytorch, util
label_desc: Use image node_id 1 to create a new CheckpointLoadContext object from inherited base classes: pytorch with example: obj = CheckpointLoadContext(hparams, exp_conf)