code: def find_checkpoint_rank_0(
      checkpoints_path, iteration, release=False
  ):
      """Finds the checkpoint for rank 0 without knowing if we are using
      pipeline parallelism/expert parallelism or not.
  
      Since the checkpoint naming scheme changes if pipeline or expert
      parallelism is present, we need to look for both naming schemes if
      we don't know if the checkpoint has pipeline or expert parallelism.
      """
  
      # Look for checkpoint with no pipelining and no expert parallelism
      filename = get_checkpoint_name(
          checkpoints_path,
          iteration,
          release,
          pipeline_parallel=False,
          tensor_rank=0,
          pipeline_rank=0,
          expert_parallel=False,
          expert_rank=0,
      )
      if os.path.isfile(filename):
          return filename
  
      # Look for checkpoint with no pipelining and expert parallelism
      filename = get_checkpoint_name(
          checkpoints_path,
          iteration,
          release,
          pipeline_parallel=False,
          tensor_rank=0,
          pipeline_rank=0,
          expert_parallel=True,
          expert_rank=0,
      )
      if os.path.isfile(filename):
          return filename
  
      # Look for checkpoint with pipelining and no expert parallelism
      filename = get_checkpoint_name(
          checkpoints_path,
          iteration,
          release,
          pipeline_parallel=True,
          tensor_rank=0,
          pipeline_rank=0,
          expert_parallel=False,
          expert_rank=0,
      )
      if os.path.isfile(filename):
          return filename
  
      # Look for checkpoint with pipelining and expert parallelism
      filename = get_checkpoint_name(
          checkpoints_path,
          iteration,
          release,
          pipeline_parallel=True,
          tensor_rank=0,
          pipeline_rank=0,
          expert_parallel=True,
          expert_rank=0,
      )
      if os.path.isfile(filename):
          return filename
  
      return None, None
  
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 7 for calling a global function with example usage: find_checkpoint_rank_0(checkpoints_path, iteration, release) and returns: None,  None, filename, filename, filename, filename