iter_describe_data_sources ¶

def iter_describe_data_sources(
    self,
    *,
    config_overrides: Optional[MachineLearningClientConfig] = None,
    filter_variable: Optional[
        "capo_machine_learning.types.data_source_filter_variable.DataSourceFilterVariable"
    ] = None,
    eq: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    gt: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    lt: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    ge: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    le: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    ne: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    prefix: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    sort_order: Optional["capo_machine_learning.types.sort_order.SortOrder"] = None,
    next_token: Optional[
        "capo_machine_learning.types.string_type.StringType"
    ] = None,
    limit: Optional["capo_machine_learning.types.page_limit.PageLimit"] = None,
) -> "Iterator[capo_machine_learning.types.data_source.DataSource]":
    _token = next_token
    while True:
        _response = self.describe_data_sources(
            config_overrides=config_overrides,
            filter_variable=filter_variable,
            eq=eq,
            gt=gt,
            lt=lt,
            ge=ge,
            le=le,
            ne=ne,
            prefix=prefix,
            sort_order=sort_order,
            next_token=_token,
            limit=limit,
        )
        _page = _resolve_path(_response, ("results",))
        for _item in _page or []:
            yield _item
        _token = _resolve_path(_response, ("next_token",))
        if not _token:
            break