code: def get_expert_model_parallel_rank():
      """Return my rank for the expert parallel group"""
      if (
          torch.distributed.is_available()
          and torch.distributed.is_initialized()
      ):
          tensor_and_expert_parallel_rank = torch.distributed.get_rank(
              group=get_tensor_and_expert_parallel_group()
          )
          return (
              tensor_and_expert_parallel_rank
              // get_tensor_model_parallel_world_size()
          )
      else:
          return 0
  
imports: import os
  from typing import Optional
  import torch
  from .utils import GlobalMemoryBuffer
label_desc: Use image node_id 51 for calling a global function with example usage: get_expert_model_parallel_rank() and returns: unknown, int