code: def __post_init__(self):
      """Python dataclass method that is used to modify attributes after initialization.
      See https://docs.python.org/3/library/dataclasses.html#post-init-processing for more details.
      """
      if self.sequence_parallel:
          if self.tensor_model_parallel_size <= 1:
              raise ValueError(
                  "Can not use sequence paralllelism without tensor parallelism"
              )
          if self.async_tensor_model_parallel_allreduce:
              # sequence_parallelism already does this async
              self.async_tensor_model_parallel_allreduce = False
  
      if self.pipeline_model_parallel_size > 1:
          if self.pipeline_dtype is None:
              raise ValueError(
                  "When using pipeline parallelism, pipeline_dtype must be specified"
              )
  
      if self.autocast_dtype is None:
          self.autocast_dtype = self.params_dtype
  
      if (
          self.expert_model_parallel_size > 1
          and self.tensor_model_parallel_size > 1
      ):
          if self.sequence_parallel is False:
              raise ValueError(
                  "When using expert parallelism and tensor parallelism, sequence parallelism must be used"
              )
  
imports: from dataclasses import dataclass
  from typing import Callable, Optional
  import torch
label_desc: Use image node_id 1 for calling the ModelParallelConfig obj's underlying member method code with example usage: obj.__post_init__() without return types