code: def get_checkpoint_name(
      checkpoints_path,
      iteration,
      release=False,
      pipeline_parallel=None,
      tensor_rank=None,
      pipeline_rank=None,
      expert_parallel=None,
      expert_rank=None,
  ):
      """Determine the directory name for this rank's checkpoint."""
      if release:
          directory = "release"
      else:
          directory = "iter_{:07d}".format(iteration)
  
      # Use both the tensor and pipeline MP rank.
      if pipeline_parallel is None:
          pipeline_parallel = (
              mpu.get_pipeline_model_parallel_world_size() > 1
          )
      if tensor_rank is None:
          tensor_rank = mpu.get_tensor_model_parallel_rank()
      if pipeline_rank is None:
          pipeline_rank = mpu.get_pipeline_model_parallel_rank()
      if expert_parallel is None:
          expert_parallel = (
              mpu.get_expert_model_parallel_world_size() > 1
          )
      if expert_rank is None:
          expert_rank = mpu.get_expert_model_parallel_rank()
  
      # Use both the tensor and pipeline MP rank. If using the distributed
      # optimizer, then the optimizer's path must additionally include the
      # data parallel rank.
      if not pipeline_parallel:
          common_path = os.path.join(
              checkpoints_path, directory, f"mp_rank_{tensor_rank:02d}"
          )
      else:
          common_path = os.path.join(
              checkpoints_path,
              directory,
              f"mp_rank_{tensor_rank:02d}_{pipeline_rank:03d}",
          )
  
      if expert_parallel:
          common_path = common_path + f"_{expert_rank:03d}"
  
      return os.path.join(common_path, "model_optim_rng.pt")
  
imports: import os
  import random
  import sys
  import numpy
  import torch
  from megatron import update_num_microbatches
  from megatron.core import mpu, tensor_parallel
  from .global_vars import get_args
  from .utils import unwrap_model, print_rank_0
label_desc: Use image node_id 5 for calling a global function with example usage: get_checkpoint_name(checkpoints_path, iteration, release, pipeline_parallel, tensor_rank, pipeline_rank, expert_parallel, expert_rank) and returns: os