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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def deserialize(cls, file_path, **kwargs):
 """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. """

        # 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