code: def prepare_environment(params: 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`
          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: import hashlib
  import io
  from datetime import timedelta
  import importlib
  import json
  import logging
  import os
  import pkgutil
  import random
  import sys
  import signal
  from contextlib import contextmanager
  from itertools import islice, zip_longest
  from pathlib import Path
  from typing import Any, Callable, Dict, Generator, Iterable, Iterator, List, Optional, Tuple, TypeVar, Union, Sequence, Set
  import dill
  import numpy
  import spacy
  import torch
  import torch.distributed
  from spacy.cli.download import download
  from spacy.language import Language
  import base58
  from allennlp.common.checks import log_pytorch_version_info
  from allennlp.common.params import Params
label_desc: Use image node_id 7 for calling a global function with example usage: prepare_environment(params) without return types