code: def _validate_args(self, mpu, replace_with_kernel_inject):
      # TODO: to support SD pipeline we need to avoid this check for now
      if replace_with_kernel_inject and not isinstance(
          self.module, Module
      ):
          raise ValueError(
              f"model must be a torch.nn.Module, got {type(self.module)}"
          )
      if (
          not isinstance(self._config.tensor_parallel.tp_size, int)
          or self._config.tensor_parallel.tp_size < 1
      ):
          raise ValueError(
              f"mp_size must be an int >= 1, got {self._config.tensor_parallel.tp_size}"
          )
  
      if mpu:
          methods = [
              "get_model_parallel_group",
              "get_data_parallel_group",
          ]
          for method in methods:
              if not hasattr(mpu, method):
                  raise ValueError(f"mpu is missing {method}")
      if self._config.checkpoint is not None and not isinstance(
          self._config.checkpoint, (str, dict)
      ):
          raise ValueError(
              f"checkpoint must be None, str or dict, got {type(self._config.checkpoint)}"
          )
  
      supported_dtypes = [None, torch.half, torch.int8, torch.float]
      if self._config.dtype not in supported_dtypes:
          raise ValueError(
              f"{self._config.dtype} not supported, valid dtype: {supported_dtypes}"
          )
  
      if self.injection_dict is not None and not isinstance(
          self.injection_dict, dict
      ):
          raise ValueError(
              f"injection_dict must be None or a dict, got: {self.injection_dict}"
          )
  
imports: import torch
  import time
  import os
  from deepspeed import comm
  from deepspeed.utils.logging import log_dist
  from torch.nn.modules import Module
  from packaging import version
  from deepspeed.runtime.checkpoint_engine.torch_checkpoint_engine import TorchCheckpointEngine
  from deepspeed.utils.timer import SynchronizedWallClockTimer
  from ..runtime.state_dict_factory import SDLoaderFactory
  from ..runtime.weight_quantizer import WeightQuantization
  from ..module_inject import replace_transformer_layer, generic_injection
  from ..comm.comm import init_distributed
  from ..pipe import PipelineModule
  from ..moe.utils import has_moe_layers
  from ..module_inject import LinearAllreduce, LinearLayer, Normalize, ReplaceWithTensorSlicing
  from deepspeed.accelerator import get_accelerator
  from ..module_inject.policy import TransformerPolicy
  from ..module_inject.auto_tp import AutoTP
  from ..module_inject.replace_policy import generic_policies
  from ..module_inject.auto_tp_model_utils import build_bloom_alibi_tensor, build_mpt_atten_bias_tensor, build_mpt_alibi_tensor, get_alibi_mask
  from ..ops.transformer.inference.ds_attention import DeepSpeedSelfAttention
  from ..model_implementations.transformers.ds_transformer import DeepSpeedTransformerInference
  from torch import nn
label_desc: Use image node_id 13 for calling the InferenceEngine obj's underlying member method code with example usage: obj._validate_args(mpu, replace_with_kernel_inject) without return types