code: def initialize(
      args=None,
      model: torch.nn.Module = None,
      optimizer: Optional[
          Union[Optimizer, DeepSpeedOptimizerCallable]
      ] = None,
      model_parameters: Optional[torch.nn.Module] = None,
      training_data: Optional[torch.utils.data.Dataset] = None,
      lr_scheduler: Optional[
          Union[_LRScheduler, DeepSpeedSchedulerCallable]
      ] = None,
      mpu=None,
      dist_init_required: Optional[bool] = None,
      collate_fn=None,
      config=None,
      config_params=None,
  ):
      """Initialize the DeepSpeed Engine.
  
      Arguments:
          args: an object containing local_rank and deepspeed_config fields.
              This is optional if `config` is passed.
  
          model: Required: nn.module class before apply any wrappers
  
          optimizer: Optional: a user defined Optimizer or Callable that returns an Optimizer object.
              This overrides any optimizer definition in the DeepSpeed json config.
  
          model_parameters: Optional: An iterable of torch.Tensors or dicts.
              Specifies what Tensors should be optimized.
  
          training_data: Optional: Dataset of type torch.utils.data.Dataset
  
          lr_scheduler: Optional: Learning Rate Scheduler Object or a Callable that takes an Optimizer and returns a Scheduler object.
              The scheduler object should define a get_lr(), step(), state_dict(), and load_state_dict() methods
  
          mpu: Optional: A model parallelism unit object that implements
              get_{model,data}_parallel_{rank,group,world_size}()
  
          dist_init_required: Optional: None will auto-initialize torch distributed if needed,
              otherwise the user can force it to be initialized or not via boolean.
  
          collate_fn: Optional: Merges a list of samples to form a
              mini-batch of Tensor(s).  Used when using batched loading from a
              map-style dataset.
  
          config: Optional: Instead of requiring args.deepspeed_config you can pass your deepspeed config
              as an argument instead, as a path or a dictionary.
  
          config_params: Optional: Same as `config`, kept for backwards compatibility.
  
      Returns:
          A tuple of ``engine``, ``optimizer``, ``training_dataloader``, ``lr_scheduler``
  
          * ``engine``: DeepSpeed runtime engine which wraps the client model for distributed training.
  
          * ``optimizer``: Wrapped optimizer if a user defined ``optimizer`` is supplied, or if
            optimizer is specified in json config else ``None``.
  
          * ``training_dataloader``: DeepSpeed dataloader if ``training_data`` was supplied,
            otherwise ``None``.
  
          * ``lr_scheduler``: Wrapped lr scheduler if user ``lr_scheduler`` is passed, or
            if ``lr_scheduler`` specified in JSON configuration. Otherwise ``None``.
      """
      log_dist(
          "DeepSpeed info: version={}, git-hash={}, git-branch={}".format(
              __version__, __git_hash__, __git_branch__
          ),
          ranks=[0],
      )
  
      # Disable zero.Init context if it's currently enabled
      zero.partition_parameters.shutdown_init_context()
  
      assert model is not None, "deepspeed.initialize requires a model"
  
      global dist
      from deepspeed import comm as dist
  
      dist_backend = get_accelerator().communication_backend_name()
      dist.init_distributed(
          dist_backend=dist_backend,
          dist_init_required=dist_init_required,
      )
  
      # Set config using config_params for backwards compat
      if config is None and config_params is not None:
          config = config_params
  
      # Check for deepscale_config for backwards compat
      if (
          hasattr(args, "deepscale_config")
          and args.deepscale_config is not None
      ):
          logger.warning(
              "************ --deepscale_config is deprecated, please use --deepspeed_config ************"
          )
          if hasattr(args, "deepspeed_config"):
              assert (
                  args.deepspeed_config is None
              ), "Not sure how to proceed, we were given both a deepscale_config and deepspeed_config"
          args.deepspeed_config = args.deepscale_config
          args.deepscale_config = None
  
      # Check that we have only one config passed
      if (
          hasattr(args, "deepspeed_config")
          and args.deepspeed_config is not None
      ):
          assert (
              config is None
          ), "Not sure how to proceed, we were given deepspeed configs in the deepspeed arguments and deepspeed.initialize() function call"
          config = args.deepspeed_config
      assert (
          config is not None
      ), "DeepSpeed requires --deepspeed_config to specify configuration file"
  
      if not isinstance(model, PipelineModule):
          config_class = DeepSpeedConfig(config, mpu)
          if config_class.hybrid_engine.enabled:
              engine = DeepSpeedHybridEngine(
                  args=args,
                  model=model,
                  optimizer=optimizer,
                  model_parameters=model_parameters,
                  training_data=training_data,
                  lr_scheduler=lr_scheduler,
                  mpu=mpu,
                  dist_init_required=dist_init_required,
                  collate_fn=collate_fn,
                  config=config,
                  config_class=config_class,
              )
          else:
              engine = DeepSpeedEngine(
                  args=args,
                  model=model,
                  optimizer=optimizer,
                  model_parameters=model_parameters,
                  training_data=training_data,
                  lr_scheduler=lr_scheduler,
                  mpu=mpu,
                  dist_init_required=dist_init_required,
                  collate_fn=collate_fn,
                  config=config,
                  config_class=config_class,
              )
      else:
          assert (
              mpu is None
          ), "mpu must be None with pipeline parallelism"
          mpu = model.mpu()
          config_class = DeepSpeedConfig(config, mpu)
          engine = PipelineEngine(
              args=args,
              model=model,
              optimizer=optimizer,
              model_parameters=model_parameters,
              training_data=training_data,
              lr_scheduler=lr_scheduler,
              mpu=mpu,
              dist_init_required=dist_init_required,
              collate_fn=collate_fn,
              config=config,
              config_class=config_class,
          )
  
      # Restore zero.Init context if necessary
      zero.partition_parameters.restore_init_context()
  
      return_items = [
          engine,
          engine.optimizer,
          engine.training_dataloader,
          engine.lr_scheduler,
      ]
      return tuple(return_items)
  
imports: import sys
  import types
  import json
  from typing import Optional, Union
  import torch
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from packaging import version
  from .None import ops
  from .None import module_inject
  from .accelerator import get_accelerator
  from .runtime.engine import DeepSpeedEngine, DeepSpeedOptimizerCallable, DeepSpeedSchedulerCallable
  from .runtime.engine import ADAM_OPTIMIZER, LAMB_OPTIMIZER
  from .runtime.hybrid_engine import DeepSpeedHybridEngine
  from .runtime.pipe.engine import PipelineEngine
  from .inference.engine import InferenceEngine
  from .inference.config import DeepSpeedInferenceConfig
  from .runtime.lr_schedules import add_tuning_arguments
  from .runtime.config import DeepSpeedConfig, DeepSpeedConfigError
  from .runtime.activation_checkpointing import checkpointing
  from .ops.transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
  from .module_inject import replace_transformer_layer, revert_transformer_layer
  from .utils import log_dist, OnDevice, logger
  from .comm.comm import init_distributed
  from .runtime import zero
  from .runtime import DeepSpeedOptimizer, ZeROOptimizer
  from .pipe import PipelineModule
  from .git_version_info import version, git_hash, git_branch
label_desc: Use image node_id 2 for calling a global function with example usage: initialize(args, model, optimizer, model_parameters, training_data, lr_scheduler, mpu, dist_init_required, collate_fn, config, config_params) and returns: tuple