class Cifar10(Dataset):
    mode: Any = ...
    backend: Any = ...
    data_file: Any = ...
    transform: Any = ...
    dtype: Any = ...

from __future__ import annotations
import numpy as np
import numpy.typing as npt
from typing import overload, TypeVar, Any, Sequence, Callable, Tuple, Union
class Tensor: ...
class PILImage: ...
_DataT = TypeVar("_DataT", Tensor, PILImage, npt.NDArray[Any])
# ---------- transform ----------
_TransformTensor = Callable[[Tensor], Tensor]
_TransformTensors = Callable[[Tuple[Tensor, ...]], Tuple[Tensor, ...]]
_TransformPILImage = Callable[[PILImage], PILImage]
_TransformPILImages = Callable[[Tuple[PILImage, ...]], Tuple[PILImage, ...]]
_TransformNDArray = Callable[[npt.NDArray[Any]], npt.NDArray[Any]]
_TransformNDArrays = Callable[
    [Tuple[npt.NDArray[Any], ...]], Tuple[npt.NDArray[Any], ...]
]
_Transform = Union[
    _TransformTensor,
    _TransformTensors,
    _TransformPILImage,
    _TransformPILImages,
    _TransformNDArray,
    _TransformNDArrays,
]
# ---------- transform end ----------
class BaseTransform:
    def __init__(self, keys=None):
        self.keys = keys
    @overload
    def __call__(self, inputs: _DataT) -> _DataT: ...
    @overload
    def __call__(self, inputs: tuple[_DataT, ...]) -> tuple[_DataT, ...]: ...
    def __call__(self, inputs):
        if isinstance(inputs, (Tensor, PILImage, np.ndarray)):
            return inputs
        return tuple(inputs)
class Resize(BaseTransform):
    def __init__(self, size, interpolation="bilinear", keys=None): ...
    @overload
    def __call__(self, inputs: _DataT) -> _DataT: ...
    @overload
    def __call__(self, inputs: tuple[_DataT, ...]) -> tuple[_DataT, ...]: ...
    def __call__(self, inputs):
        if isinstance(inputs, (Tensor, PILImage, np.ndarray)):
            return inputs
        return tuple(inputs)
class Compose:
    transforms: Sequence[BaseTransform]
    def __init__(self, transforms: Sequence[BaseTransform]) -> None:
        self.transforms = transforms
    @overload
    def __call__(self, data: _DataT) -> _DataT: ...
    @overload
    def __call__(self, data: tuple[_DataT, ...]) -> tuple[_DataT, ...]: ...
    def __call__(self, data):
        if isinstance(data, (Tensor, PILImage, np.ndarray)):
            return data
        return tuple(data)
class Cifar10:
    transform: _Transform
    def __init__(self, transform: _Transform) -> None:
        self.transform = transform
t1 = Resize(1)
cifar = Cifar10(transform=t1) # ok
t2 = Compose([Resize(1), Resize(2)])
cifar = Cifar10(transform=t2) # ok
def trans_pass(a: Tensor) -> Tensor:
    return Tensor()
def trans_fail(a: Tensor) -> tuple[Tensor, Tensor]:
    return Tensor(), Tensor()
cifar = Cifar10(transform=trans_pass) # ok
cifar = Cifar10(transform=trans_fail) # This should fail