code: def get_embeddings_index(embedding_type='glove.42B.300d', embedding_dims=None, embedding_path=None, cache=True):
      
  
      if embedding_path is not None:
          embedding_type = embedding_path  # identify embedding by path
  
      embeddings_index = _EMBEDDINGS_CACHE.get(embedding_type)
      if embeddings_index is not None:
          return embeddings_index
  
      if embedding_path is None:
          embedding_type_obj = get_embedding_type(embedding_type)
  
          # some very rough wrangling of zip files with the keras util `get_file`
          # a special problem: when multiple files are in one zip file
          extract = embedding_type_obj.get('extract', True)
          file_path = get_file(
              embedding_type_obj['file'], origin=embedding_type_obj['url'], extract=extract, cache_subdir='embeddings', file_hash=embedding_type_obj.get('file_hash',))
  
          if 'file_in_zip' in embedding_type_obj:
              zip_folder = file_path.split('.zip')[0]
              with ZipFile(file_path, 'r') as zf:
                  zf.extractall(zip_folder)
              file_path = os.path.join(
                  zip_folder, embedding_type_obj['file_in_zip'])
          else:
              if extract:
                  if file_path.endswith('.zip'):
                      file_path = file_path.split('.zip')[0]
                  # if file_path.endswith('.gz'):
                  #     file_path = file_path.split('.gz')[0]
      else:
          file_path = embedding_path
  
      embeddings_index = _build_embeddings_index(file_path, embedding_dims)
  
      if cache:
          _EMBEDDINGS_CACHE[embedding_type] = embeddings_index
      return embeddings_index
docs: Retrieves embeddings index from embedding name or path. Will automatically download and cache as needed.
  
      Args:
          embedding_type: The embedding type to load.
          embedding_path: Path to a local embedding to use instead of the embedding type. Ignores `embedding_type` if specified.
  
      Returns:
          The embeddings indexed by word.