class OptimizedTypedSequence(TypedSequence):
    def __init__(
        self,
        data,
        type: Optional[FeatureType] = None,
        try_type: Optional[FeatureType] = None,
        col: Optional[str] = None,
        optimized_int_type: Optional[FeatureType] = None,
    ):
        optimized_int_type_by_col = {
            "attention_mask": Value("int8"),  # binary tensor
            "special_tokens_mask": Value("int8"),
            "input_ids": Value("int32"),  # typical vocab size: 0-50k (max ~500k, never > 1M)
            "token_type_ids": Value(
                "int8"
            ),  # binary mask; some (XLNetModel) use an additional token represented by a 2
        }
        if type is None and try_type is None:
            optimized_int_type = optimized_int_type_by_col.get(col, None)
        super().__init__(data, type=type, try_type=try_type, optimized_int_type=optimized_int_type)
/Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_writer.py