code: def load_glove_embedding(
      dimension: int = 50,
  ) -> mz.embedding.Embedding:
      """
      Return the pretrained glove embedding.
  
      :param dimension: the size of embedding dimension, the value can only be
          50, 100, or 300.
      :return: The :class:`mz.embedding.Embedding` object.
      """
      file_name = "glove.6B." + str(dimension) + "d.txt"
      file_path = (Path(mz.USER_DATA_DIR) / "glove").joinpath(file_name)
      if not file_path.exists():
          keras.utils.data_utils.get_file(
              "glove_embedding",
              _glove_embedding_url,
              extract=True,
              cache_dir=mz.USER_DATA_DIR,
              cache_subdir="glove",
          )
      return mz.embedding.load_from_file(
          file_path=str(file_path), mode="glove"
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_glove_embedding(dimension) and returns: mz