code: def convert_to_required_dtype(self):
      # Note: converting tensors to fp16 requires that we do it in-place using self.__dict__ and not make a list/dict copy
      if self.dtype in [torch.half, torch.bfloat16]:
          for k, v in self.__dict__.items():
              # The list comprehension is used for MoE tensor lists
              if isinstance(v, list) and all(
                  (
                      isinstance(tensor, torch.Tensor)
                      or isinstance(tensor, torch.nn.Parameter)
                  )
                  for tensor in v
              ):
                  self.__dict__[k] = [
                      moe_tensor.to(self.dtype) for moe_tensor in v
                  ]
  
              if isinstance(v, torch.Tensor) or isinstance(
                  v, torch.nn.Parameter
              ):
                  self.__dict__[k] = v.to(self.dtype)
  
imports: from abc import ABC
  import torch
  import deepspeed
  from deepspeed.ops.transformer.inference.config import DeepSpeedInferenceConfig
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 5 for calling the BaseTransformerContainer obj's underlying member method code with example usage: obj.convert_to_required_dtype() without return types