code: def deserialize(cls, file_path, **kwargs):
          
          # idx_to_token is of dtype 'O' so we need to allow pickle
          npz_dict = np.load(file_path, allow_pickle=True)
  
          unknown_token = npz_dict['unknown_token']
          if not unknown_token:
              unknown_token = None
          else:
              if isinstance(unknown_token, np.ndarray):
                  if unknown_token.dtype.kind == 'S':
                      unknown_token = unknown_token.tobytes().decode()
                  else:
                      unknown_token = str(unknown_token)
          idx_to_token = npz_dict['idx_to_token'].tolist()
          idx_to_vec = nd.array(npz_dict['idx_to_vec'])
  
          embedding = cls(unknown_token=unknown_token, **kwargs)
          if unknown_token:
              assert unknown_token == idx_to_token[C.UNK_IDX]
              embedding._token_to_idx = DefaultLookupDict(C.UNK_IDX)
          else:
              embedding._token_to_idx = {}
  
          embedding._idx_to_token = idx_to_token
          embedding._idx_to_vec = idx_to_vec
          embedding._token_to_idx.update((token, idx) for idx, token in enumerate(idx_to_token))
  
          return embedding
docs: Create a new TokenEmbedding from a serialized one.
  
          TokenEmbedding is serialized by converting the list of tokens, the
          array of word embeddings and other metadata to numpy arrays, saving all
          in a single (optionally compressed) Zipfile. See
          https://docs.scipy.org/doc/numpy-1.14.2/neps/npy-format.html for more
          information on the format.
  
  
          Parameters
          ----------
          file_path : str or file
              The path to a file that holds the serialized TokenEmbedding.
          kwargs : dict
              Keyword arguments are passed to the TokenEmbedding initializer.
              Useful for attaching unknown_lookup.