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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 _load_embedding(self, pretrained_file_path, elem_delim, init_unknown_vec, encoding='utf8'):
 """Load embedding vectors from the pre-trained token embedding file. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre-trained token embedding vector loaded from the file; otherwise, index 0 of `self.idx_to_vec` maps to the text embedding vector initialized by `init_unknown_vec`. If a token is encountered multiple times in the pre-trained text embedding file, only the first-encountered token embedding vector will be loaded and the rest will be skipped. """


        pretrained_file_path = os.path.expanduser(pretrained_file_path)

        if not os.path.isfile(pretrained_file_path):
            raise ValueError('`pretrained_file_path` must be a valid path to '
                             'the pre-trained token embedding file.')

        logging.info('Loading pre-trained token embedding vectors from %s', pretrained_file_path)
        vec_len = None
        all_elems = []
        tokens = set()
        loaded_unknown_vec = None
        line_num = 0
        with io.open(pretrained_file_path, 'r', encoding=encoding) as f:
            for line in f:
                line_num += 1
                elems = line.rstrip().split(elem_delim)

                assert len(elems) > 1, 'At line %d of the pre-trained text embedding file: the ' \
                                       'data format of the pre-trained token embedding file %s ' \
                                       'is unexpected.' % (line_num, pretrained_file_path)

                token, elems = elems[0], [float(i) for i in elems[1:]]

                if token == self.unknown_token and loaded_unknown_vec is None:
                    loaded_unknown_vec = elems
                    tokens.add(self.unknown_token)
                elif token in tokens:
                    warnings.warn('At line %d of the pre-trained token embedding file: the '
                                  'embedding vector for token %s has been loaded and a duplicate '
                                  'embedding for the  same token is seen and skipped.' %
                                  (line_num, token))
                elif len(elems) == 1:
                    warnings.warn('At line %d of the pre-trained text embedding file: token %s '
                                  'with 1-dimensional vector %s is likely a header and is '
                                  'skipped.' % (line_num, token, elems))
                else:
                    if vec_len is None:
                        vec_len = len(elems)
                        # Reserve a vector slot for the unknown token at the very beggining because
                        # the unknown index is 0.
                        all_elems.extend([0] * vec_len)
                    else:
                        assert len(elems) == vec_len, \
                            'At line %d of the pre-trained token embedding file: the dimension ' \
                            'of token %s is %d but the dimension of previous tokens is %d. ' \
                            'Dimensions of all the tokens must be the same.' \
                            % (line_num, token, len(elems), vec_len)
                    all_elems.extend(elems)
                    self._idx_to_token.append(token)
                    self._token_to_idx[token] = len(self._idx_to_token) - 1
                    tokens.add(token)

        self._vec_len = vec_len
        self._idx_to_vec = nd.array(all_elems).reshape((-1, self.vec_len))

        if loaded_unknown_vec is None:
            self._idx_to_vec[C.UNKNOWN_IDX] = init_unknown_vec(shape=self.vec_len)
        else:
            self._idx_to_vec[C.UNKNOWN_IDX] = nd.array(loaded_unknown_vec)