code: def get_rng_state():
      """collect rng state across data parallel ranks"""
      args = get_args()
      rng_state = {
          "random_rng_state": random.getstate(),
          "np_rng_state": np.random.get_state(),
          "torch_rng_state": torch.get_rng_state(),
          "cuda_rng_state": torch.cuda.get_rng_state(),
          "rng_tracker_states": tensor_parallel.get_cuda_rng_tracker().get_states(),
      }
  
      rng_state_list = None
      if (
          torch.distributed.is_initialized()
          and mpu.get_data_parallel_world_size() > 1
          and args.data_parallel_random_init
      ):
          rng_state_list = [
              None for i in range(mpu.get_data_parallel_world_size())
          ]
          torch.distributed.all_gather_object(
              rng_state_list,
              rng_state,
              group=mpu.get_data_parallel_group(),
          )
      else:
          rng_state_list = [rng_state]
  
      return rng_state_list
  
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 10 for calling a global function with example usage: get_rng_state() and returns: rng_state_list