code: def load_pretrained_params(
      model: Model,
      url: Optional[str] = None,
      hash_prefix: Optional[str] = None,
      overwrite: bool = False,
      internal_name: str = "weights",
      **kwargs: Any,
  ) -> None:
      """Load a set of parameters onto a model
  
      >>> from doctr.models import load_pretrained_params
      >>> load_pretrained_params(model, "https://yoursource.com/yourcheckpoint-yourhash.zip")
  
      Args:
      ----
          model: the keras model to be loaded
          url: URL of the zipped set of parameters
          hash_prefix: first characters of SHA256 expected hash
          overwrite: should the zip extraction be enforced if the archive has already been extracted
          internal_name: name of the ckpt files
          **kwargs: additional arguments to be passed to `doctr.utils.data.download_from_url`
      """
      if url is None:
          logging.warning(
              "Invalid model URL, using default initialization."
          )
      else:
          archive_path = download_from_url(
              url,
              hash_prefix=hash_prefix,
              cache_subdir="models",
              **kwargs,
          )
  
          # Unzip the archive
          params_path = archive_path.parent.joinpath(archive_path.stem)
          if not params_path.is_dir() or overwrite:
              with ZipFile(archive_path, "r") as f:
                  f.extractall(path=params_path)
  
          # Load weights
          model.load_weights(f"{params_path}{os.sep}{internal_name}")
  
file_path: doctr/doctr/models/utils/tensorflow.py
imports: import logging
  import os
  from typing import Any, Callable, List, Optional, Tuple, Union
  from zipfile import ZipFile
  import tensorflow
  import tf2onnx
  from tensorflow.keras import Model, layers
  from doctr.utils.data import download_from_url
label_desc: Use image node_id 3 for calling a global function with example usage: load_pretrained_params(model, url, hash_prefix, overwrite, internal_name) without return types