How to use the simpletransformers.classification.classification_utils.InputFeatures function in simpletransformers

input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
            segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        # if output_mode == "classification":
        #     label_id = label_map[example.label]
        # elif output_mode == "regression":
        #     label_id = float(example.label)
        # else:
        #     raise KeyError(output_mode)

        input_features.append(
            InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_id=example.label
            )
        )

    return input_features

input_ids = input_ids + ([pad_token] * padding_length)
        input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
        segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length

    # if output_mode == "classification":
    #     label_id = label_map[example.label]
    # elif output_mode == "regression":
    #     label_id = float(example.label)
    # else:
    #     raise KeyError(output_mode)

    return InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=example.label
    )

input_ids = input_ids + ([pad_token] * padding_length)
        input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
        segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length

    # if output_mode == "classification":
    #     label_id = label_map[example.label]
    # elif output_mode == "regression":
    #     label_id = float(example.label)
    # else:
    #     raise KeyError(output_mode)

    return InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=example.label
    )