code: def _convert_to_dtype(self, config):
      if not isinstance(self.module, torch.nn.Module):
          return
  
      if (
          False
      ):  # config.dtype is torch.int8 and self.quantization_scales is None:
          quantizer = WeightQuantization(
              mlp_extra_grouping=self.mlp_extra_grouping
          )
          model, self.quantization_scales = quantizer.model_quantize(
              self.module,
              self.injection_dict,
              self.quantize_bits,
              self.quantize_groups,
          )
      elif config.dtype == torch.half:
          self.module.half()
      elif config.dtype == torch.bfloat16:
          self.module.bfloat16()
      elif config.dtype == torch.float:
          self.module.float()
  
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 20 for calling the InferenceEngine obj's underlying member method code with example usage: obj._convert_to_dtype(config) without return types