code: def run(self) -> None:
      @contextlib.contextmanager
      def defer(fn: Callable, *args: Any) -> Iterator[None]:
          try:
              yield
          finally:
              fn(*args)
  
      # We define on_shutdown here instead of inside the `for callback in...` loop to ensure we
      # don't bind the loop iteration variable `callback`, which would likely cause us to call
      # on_trial_shutdown() multiple times for the final callback, and not at all for the others.
      def on_shutdown(
          callback_name: str, on_trial_shutdown: Callable
      ) -> None:
          with self.prof.record_timing(
              f"callbacks.{callback_name}.on_trial_shutdown"
          ):
              on_trial_shutdown()
  
      with contextlib.ExitStack() as exit_stack:
          for callback in self.callbacks.values():
              with self.prof.record_timing(
                  f"callbacks.{callback.__class__.__name__}.on_trial_startup"
              ):
                  callback.on_trial_startup(
                      self.steps_completed, self.env.latest_checkpoint
                  )
              exit_stack.enter_context(
                  defer(
                      on_shutdown,
                      callback.__class__.__name__,
                      callback.on_trial_shutdown,
                  )
              )
  
          logger.info(self.context._mpu)
  
          self._set_data_loaders()
  
          # We create the training_iterator here rather than in __init__ because we have to be
          # careful to trigger its shutdown explicitly, to avoid hangs in when the user is using
          # multiprocessing-based parallelism for their data loader.
          #
          # We create it before loading state because we don't want the training_iterator
          # shuffling values after we load state.
          self.training_iterator = (
              iter(self.training_loader)
              if self.training_loader is not None
              else None
          )
  
          def cleanup_iterator() -> None:
              # Explicitly trigger the training iterator's shutdown (which happens in __del__).
              # See the rather long note in pytorch/torch/utils/data/dataloader.py.
              del self.training_iterator
  
          exit_stack.enter_context(defer(cleanup_iterator))
  
          # If a load path is provided load weights and restore the data location.
          if self.env.latest_checkpoint is not None:
              logger.info(
                  f"Restoring trial from checkpoint {self.env.latest_checkpoint}"
              )
              with self.context._core.checkpoint.restore_path(
                  self.env.latest_checkpoint
              ) as load_path:
                  self._load(load_path)
  
          with self.prof:
              for callback in self.callbacks.values():
                  with self.prof.record_timing(
                      f"callbacks.{callback.__class__.__name__}.on_training_start"
                  ):
                      callback.on_training_start()
              self._run()
  
imports: import abc
  import contextlib
  import logging
  import os
  import pathlib
  import pickle
  import random
  import time
  import warnings
  from typing import Any, Callable, Dict, Iterator, List, Optional, Type, Union, cast
  import deepspeed
  import numpy
  import torch
  from deepspeed.runtime import dataloader
  import determined
  from determined import layers, pytorch, util, workload
  from determined.pytorch import deepspeed
  from determined.pytorch import dsat
label_desc: Use image node_id 5 for calling the DeepSpeedTrialController obj's underlying member method code with example usage: obj.run() without return types