code: def init_inference(model, config=None, **kwargs):
      """Initialize the DeepSpeed InferenceEngine.
  
      Description: all four cases are valid and supported in DS init_inference() API.
  
      # Case 1: user provides no config and no kwargs. Default config will be used.
  
      .. code-block:: python
  
          generator.model = deepspeed.init_inference(generator.model)
          string = generator("DeepSpeed is")
          print(string)
  
      # Case 2: user provides a config and no kwargs. User supplied config will be used.
  
      .. code-block:: python
  
          generator.model = deepspeed.init_inference(generator.model, config=config)
          string = generator("DeepSpeed is")
          print(string)
  
      # Case 3: user provides no config and uses keyword arguments (kwargs) only.
  
      .. code-block:: python
  
          generator.model = deepspeed.init_inference(generator.model,
                                                      mp_size=world_size,
                                                      dtype=torch.half,
                                                      replace_with_kernel_inject=True)
          string = generator("DeepSpeed is")
          print(string)
  
      # Case 4: user provides config and keyword arguments (kwargs). Both config and kwargs are merged and kwargs take precedence.
  
      .. code-block:: python
  
          generator.model = deepspeed.init_inference(generator.model, config={"dtype": torch.half}, replace_with_kernel_inject=True)
          string = generator("DeepSpeed is")
          print(string)
  
      Arguments:
          model: Required: original nn.module object without any wrappers
  
          config: Optional: instead of arguments, you can pass in a DS inference config dict or path to JSON file
  
      Returns:
          A deepspeed.InferenceEngine wrapped model.
      """
      log_dist(
          "DeepSpeed info: version={}, git-hash={}, git-branch={}".format(
              __version__, __git_hash__, __git_branch__
          ),
          ranks=[0],
      )
  
      # Load config_dict from config first
      if config is None:
          config = {}
      if isinstance(config, str):
          with open(config, "r") as f:
              config_dict = json.load(f)
      elif isinstance(config, dict):
          config_dict = config
      else:
          raise ValueError(
              f"'config' argument expected string or dictionary, got {type(config)}"
          )
  
      # Update with values from kwargs, ensuring no conflicting overlap between config and kwargs
      overlap_keys = set(config_dict.keys()).intersection(kwargs.keys())
      # If there is overlap, error out if values are different
      for key in overlap_keys:
          if config_dict[key] != kwargs[key]:
              raise ValueError(
                  f"Conflicting argument '{key}' in 'config':{config_dict[key]} and kwargs:{kwargs[key]}"
              )
      config_dict.update(kwargs)
  
      ds_inference_config = DeepSpeedInferenceConfig(**config_dict)
  
      engine = InferenceEngine(model, config=ds_inference_config)
  
      return engine
  
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 6 for calling a global function with example usage: init_inference(model, config) and returns: engine