def map(
        self,
        function: Optional[Callable] = None,
        with_indices: bool = False,
        input_columns: Optional[Union[str, List[str]]] = None,
        batched: bool = False,
        batch_size: int = 1000,
        drop_last_batch: bool = False,
        remove_columns: Optional[Union[str, List[str]]] = None,
        fn_kwargs: Optional[dict] = None,
    ) -> "IterableDatasetDict":
        """
        Apply a function to all the examples in the iterable dataset (individually or in batches) and update them.
        If your function returns a column that already exists, then it overwrites it.
        The function is applied on-the-fly on the examples when iterating over the dataset.
        The transformation is applied to all the datasets of the dataset dictionary.

        You can specify whether the function should be batched or not with the `batched` parameter: