code: def pre_execute_hook(
      cls: Type["DeepSpeedTrialController"],
      env: det.EnvContext,
      distributed_backend: det._DistributedBackend,
  ) -> None:
      # We use an environment variable to allow users to enable custom initialization routine for
      # distributed training since the pre_execute_hook runs before trial initialization.
      manual_dist_init = os.environ.get("DET_MANUAL_INIT_DISTRIBUTED")
      if not manual_dist_init:
          # DeepSpeed's init_distributed handles situations in which only 1 gpu is used and
          # also handles multiple calls to init in one process.
          deepspeed.init_distributed(auto_mpi_discovery=False)
  
      # Set identical random seeds on all training processes.
      # When data parallel world size > 1, each data parallel rank will start at a unique
      # offset in the dataset, ensuring it's processing a unique
      # training batch.
      # TODO (Liam): seed data loading workers so that we can configure different seeds for
      # data augmentations per slot per worker.
      random.seed(env.trial_seed)
      np.random.seed(env.trial_seed)
      torch.random.manual_seed(env.trial_seed)
  
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 2 for calling the DeepSpeedTrialController obj's underlying member method code with example usage: obj.pre_execute_hook(cls, env, distributed_backend) without return types