| from typing import Any, Iterator, List, TypeVar | |
| from .pytorch import IterableDataset | |
| from .utils import PipelineStage | |
| T = TypeVar('T') | |
| def add_length_method(obj: Any) -> Any: ... | |
| class DataPipeline(IterableDataset, PipelineStage): | |
| pipeline: List[Any] | |
| length: int | |
| repetitions: int | |
| nsamples: int | |
| size: int | |
| def __init__(self, *args: Any, **kwargs: Any) -> None: ... | |
| def close(self) -> None: ... | |
| def invoke(self, f: Any, *args: Any, **kwargs: Any) -> Any: ... | |
| def iterator1(self) -> Iterator[Any]: ... | |
| def iterator(self) -> Iterator[Any]: ... | |
| def __iter__(self) -> Iterator[Any]: ... | |
| def stage(self, i: int) -> Any: ... | |
| def append(self, f: Any) -> None: ... | |
| def compose(self, *args: Any) -> 'DataPipeline': ... | |
| def with_length(self, n: int, silent: bool = False) -> 'DataPipeline': ... | |
| def with_epoch(self, nsamples: int = -1, nbatches: int = -1) -> 'DataPipeline': ... | |
| def repeat(self, nepochs: int = -1, nbatches: int = -1) -> 'DataPipeline': ... | |