def __call__(self, line):
input_ids, valid_length, segment_ids = self._bert_xform(line[:-<NUM_LIT:1>])<EOL>label = line[-<NUM_LIT:1>]<EOL>if self.labels:  <EOL><INDENT>label = self._label_map[label]<EOL><DEDENT>label = np.array([label], dtype=self.label_dtype)<EOL>return input_ids, valid_length, segment_ids, label<EOL>
Perform transformation for sequence pairs or single sequences.

        The transformation is processed in the following steps:
        - tokenize the input sequences
        - insert [CLS], [SEP] as necessary
        - generate type ids to indicate whether a token belongs to the first
          sequence or the second sequence.
        - generate valid length

        For sequence pairs, the input is a tuple of 3 strings:
        text_a, text_b and label.

        Inputs:
            text_a: 'is this jacksonville ?'
            text_b: 'no it is not'
            label: '0'
        Tokenization:
            text_a: 'is this jack ##son ##ville ?'
            text_b: 'no it is not .'
        Processed:
            tokens:  '[CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]'
            type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
            valid_length: 14
            label: 0

        For single sequences, the input is a tuple of 2 strings: text_a and label.
        Inputs:
            text_a: 'the dog is hairy .'
            label: '1'
        Tokenization:
            text_a: 'the dog is hairy .'
        Processed:
            text_a:  '[CLS] the dog is hairy . [SEP]'
            type_ids: 0     0   0   0  0     0 0
            valid_length: 7
            label: 1

        Parameters
        ----------
        line: tuple of str
            Input strings. For sequence pairs, the input is a tuple of 3 strings:
            (text_a, text_b, label). For single sequences, the input is a tuple
            of 2 strings: (text_a, label).

        Returns
        -------
        np.array: input token ids in 'int32', shape (batch_size, seq_length)
        np.array: valid length in 'int32', shape (batch_size,)
        np.array: input token type ids in 'int32', shape (batch_size, seq_length)
        np.array: classification task: label id in 'int32', shape (batch_size, 1),
            regression task: label in 'float32', shape (batch_size, 1)