code: def _set_random_seed(seed_, data_parallel_random_init=False):
      """Set random seed for reproducability."""
      if seed_ is not None and seed_ > 0:
          # Ensure that different pipeline MP stages get different seeds.
          seed = seed_ + (100 * mpu.get_pipeline_model_parallel_rank())
          # Ensure different data parallel ranks get different seeds
          if data_parallel_random_init:
              seed = seed + (10 * mpu.get_data_parallel_rank())
          random.seed(seed)
          np.random.seed(seed)
          torch.manual_seed(seed)
          if torch.cuda.device_count() > 0:
              tensor_parallel.model_parallel_cuda_manual_seed(seed)
      else:
          raise ValueError(
              "Seed ({}) should be a positive integer.".format(seed)
          )
  
imports: import random
  import os
  import time
  import numpy
  import torch
  from datetime import timedelta
  from megatron import fused_kernels
  from megatron import get_adlr_autoresume
  from megatron import get_args
  from megatron import get_tensorboard_writer
  from megatron.core import mpu, tensor_parallel
  from megatron.arguments import parse_args, validate_args
  from megatron.checkpointing import load_args_from_checkpoint
  from megatron.global_vars import set_global_variables
  from megatron.model.transformer import bias_dropout_add_fused_train
  from megatron.model.fused_bias_gelu import bias_gelu
label_desc: Use image node_id 6 for calling a global function with example usage: _set_random_seed(seed_, data_parallel_random_init) without return types