code: def load_archive(
      archive_file: Union[str, PathLike],
      cuda_device: int = -1,
      overrides: Union[str, Dict[str, Any]] = "",
      weights_file: str = None,
  ) -> Archive:
      """
      Instantiates an Archive from an archived `tar.gz` file.
  
      # Parameters
  
      archive_file : `Union[str, PathLike]`
          The archive file to load the model from.
      cuda_device : `int`, optional (default = `-1`)
          If `cuda_device` is >= 0, the model will be loaded onto the
          corresponding GPU. Otherwise it will be loaded onto the CPU.
      overrides : `Union[str, Dict[str, Any]]`, optional (default = `""`)
          JSON overrides to apply to the unarchived `Params` object.
      weights_file : `str`, optional (default = `None`)
          The weights file to use.  If unspecified, weights.th in the archive_file will be used.
      """
      # redirect to the cache, if necessary
      resolved_archive_file = cached_path(archive_file)
  
      if resolved_archive_file == archive_file:
          logger.info(f"loading archive file {archive_file}")
      else:
          logger.info(
              f"loading archive file {archive_file} from cache at {resolved_archive_file}"
          )
  
      meta: Optional[Meta] = None
  
      tempdir = None
      try:
          if os.path.isdir(resolved_archive_file):
              serialization_dir = resolved_archive_file
          else:
              with extracted_archive(
                  resolved_archive_file, cleanup=False
              ) as tempdir:
                  serialization_dir = tempdir
  
          if weights_file:
              weights_path = weights_file
          else:
              weights_path = get_weights_path(serialization_dir)
  
          # Load config
          config = Params.from_file(
              os.path.join(serialization_dir, CONFIG_NAME), overrides
          )
  
          # Instantiate model and dataset readers. Use a duplicate of the config, as it will get consumed.
          (
              dataset_reader,
              validation_dataset_reader,
          ) = _load_dataset_readers(
              config.duplicate(), serialization_dir
          )
          model = _load_model(
              config.duplicate(),
              weights_path,
              serialization_dir,
              cuda_device,
          )
  
          # Load meta.
          meta_path = os.path.join(serialization_dir, META_NAME)
          if os.path.exists(meta_path):
              meta = Meta.from_path(meta_path)
      finally:
          if tempdir is not None:
              logger.info(
                  f"removing temporary unarchived model dir at {tempdir}"
              )
              shutil.rmtree(tempdir, ignore_errors=True)
  
      # Check version compatibility.
      if meta is not None:
          _check_version_compatibility(archive_file, meta)
  
      return Archive(
          model=model,
          config=config,
          dataset_reader=dataset_reader,
          validation_dataset_reader=validation_dataset_reader,
          meta=meta,
      )
  
imports: from os import PathLike
  from pathlib import Path
  from typing import Tuple, NamedTuple, Union, Dict, Any, List, Optional
  import logging
  import os
  import tempfile
  import tarfile
  import shutil
  from contextlib import contextmanager
  import glob
  import warnings
  from torch.nn import Module
  from allennlp.version import VERSION, _MAJOR, _MINOR, _PATCH
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.common.meta import Meta, META_NAME
  from allennlp.common.params import Params
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.models.model import Model, _DEFAULT_WEIGHTS
label_desc: Use image node_id 3 for calling a global function with example usage: load_archive(archive_file, cuda_device, overrides, weights_file) and returns: Archive