code: def _set_random_seed(seed_):
      """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 * get_pipeline_model_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 numpy
  import torch
  from nemo.utils import AppState, logging
label_desc: Use image node_id 2 for calling a global function with example usage: _set_random_seed(seed_) without return types