code: def get_pipeline_model_parallel_rank():
      """Return my rank for the pipeline model parallel group."""
      global _MPU_PIPELINE_MODEL_PARALLEL_RANK
      if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None:
          return _MPU_PIPELINE_MODEL_PARALLEL_RANK
      return torch.distributed.get_rank(
          group=get_pipeline_model_parallel_group()
      )
  
imports: import os
  from typing import Optional
  import torch
  from .utils import GlobalMemoryBuffer
label_desc: Use image node_id 27 for calling a global function with example usage: get_pipeline_model_parallel_rank() and returns: torch, _MPU_PIPELINE_MODEL_PARALLEL_RANK