code: def prepare_environment(params):
      """
      Sets random seeds for reproducible experiments. This may not work as expected
      if you use this from within a python project in which you have already imported Pytorch.
      If you use the scripts/run_model.py entry point to training models with this library,
      your experiments should be reasonably reproducible. If you are using this from your own
      project, you will want to call this function before importing Pytorch. Complete determinism
      is very difficult to achieve with libraries doing optimized linear algebra due to massively
      parallel execution, which is exacerbated by using GPUs.
  
      Parameters
      ----------
      params: Params object or dict, required.
          A ``Params`` object or dict holding the json parameters.
      """
      seed = params.pop_int("random_seed", 13370)
      numpy_seed = params.pop_int("numpy_seed", 1337)
      torch_seed = params.pop_int("pytorch_seed", 133)
  
      if seed is not None:
          random.seed(seed)
      if numpy_seed is not None:
          numpy.random.seed(numpy_seed)
      if torch_seed is not None:
          torch.manual_seed(torch_seed)
          # Seed all GPUs with the same seed if available.
          if torch.cuda.is_available():
              torch.cuda.manual_seed_all(torch_seed)
  
      log_pytorch_version_info()
  
imports: from __future__ import division
  from __future__ import absolute_import
  import importlib
  import logging
  import pkgutil
  import random
  import subprocess
  import sys
  import os
  import torch
  import numpy
  import spacy
  from spacy.cli.download import download
  from spacy.language import Language
  import allennlp
  from allennlp.common.checks import log_pytorch_version_info
  from allennlp.common.params import Params
  from allennlp.common.tqdm import Tqdm
  from allennlp.common.tee_logger import TeeLogger
label_desc: Use image node_id 7 for calling a global function with example usage: prepare_environment(params) without return types