class_docstr: Configuration for the distributed execution.
  
  Args:
      pipeline_parallel_size: Number of pipeline parallel groups.
      tensor_parallel_size: Number of tensor parallel groups.
      worker_use_ray: Whether to use Ray for model workers. Will be set to
          True if either pipeline_parallel_size or tensor_parallel_size is
          greater than 1.
class_docstr_tok: ["Configuration","for","the","distributed","execution",".","Args",":","pipeline_parallel_size",":","Number","of","pipeline","parallel","groups",".","tensor_parallel_size",":","Number","of","tensor","parallel","groups",".","worker_use_ray",":","Whether","to","use","Ray","for","model","workers",".","Will","be","set","to","True","if","either","pipeline_parallel_size","or","tensor_parallel_size","is","greater","than","1","."]
code: def _verify_args(self) -> None:
      if self.pipeline_parallel_size > 1:
          invalidInputError(
              "Pipeline parallelism is not supported yet."
          )
  
imports: from typing import Optional
  import torch
  from transformers import AutoConfig, PretrainedConfig
  from bigdl.llm.vllm.logger import init_logger
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 2 for calling the ParallelConfig obj's underlying member method code with example usage: obj._verify_args() without return types