class TestDynamicDataLoaderIterInitFuncSplit(unittest.TestCase):
    def test_main(self):
        place = base.CPUPlace()
        with base.dygraph.guard(place):
            dataset = RangeIterableDataset(0, 10)
            def worker_spliter(worker_id):
                worker_info = get_worker_info()
                dataset = worker_info.dataset
                start = dataset.start
                end = dataset.end
                num_per_worker = int(
                    math.ceil((end - start) / float(worker_info.num_workers))
                )
                worker_id = worker_info.id
                dataset.start = start + worker_id * num_per_worker
                dataset.end = min(dataset.start + num_per_worker, end)
            dataloader = DataLoader(
                dataset,
                places=place,
                num_workers=1,
                batch_size=1,
                drop_last=True,
                worker_init_fn=worker_spliter,
            )
            rets = []
            for d in dataloader:
                rets.append(d.numpy()[0][0])
            assert tuple(sorted(rets)) == tuple(range(0, 10))

from __future__ import annotations
import numbers
import numpy as np
import numpy.typing as npt
from typing import Protocol, overload, Sequence, Mapping, Any, TYPE_CHECKING, AnyStr, TypeVar
import paddle
if TYPE_CHECKING:
    from paddle import Tensor
    KT = TypeVar('KT')
    VT = TypeVar('VT')
@overload
def default_collate_fn(batch: Sequence[npt.NDArray[Any]] | Sequence[numbers.Number]) -> npt.NDArray[Any]: ...
@overload
def default_collate_fn(batch: Sequence[Tensor]) -> Tensor: ...
@overload
def default_collate_fn(batch: Sequence[AnyStr]) -> AnyStr: ...
@overload
def default_collate_fn(batch: Sequence[Mapping[KT, VT]]) -> Mapping[KT, VT]: ...
@overload
def default_collate_fn(batch: Sequence[Sequence[VT]]) -> Sequence[VT]: ...
def default_collate_fn(batch):
    sample = batch[0]
    if isinstance(sample, np.ndarray):
        batch = np.stack(batch, axis=0)
        return batch
    elif isinstance(sample, paddle.Tensor):
        return paddle.stack(batch, axis=0)
    elif isinstance(sample, numbers.Number):
        batch = np.array(batch)
        return batch
    elif isinstance(sample, (str, bytes)):
        return batch
    elif isinstance(sample, Mapping):
        return {
            key: default_collate_fn([d[key] for d in batch]) for key in sample
        }
    elif isinstance(sample, Sequence):
        sample_fields_num = len(sample)
        if not all(len(sample) == sample_fields_num for sample in iter(batch)):
            raise RuntimeError(
                "fields number not same among samples in a batch"
            )
        return [default_collate_fn(fields) for fields in zip(*batch)]
    raise TypeError(
        "batch data con only contains: tensor, numpy.ndarray, "
        f"dict, list, number, but got {type(sample)}"
    )
class _Collate_Fn(Protocol):
    @overload
    def __call__(self, batch: Sequence[npt.NDArray[Any]] | Sequence[numbers.Number]) -> npt.NDArray[Any]: ...
    @overload
    def __call__(self, batch: Sequence[Tensor]) -> Tensor: ...
    @overload
    def __call__(self, batch: Sequence[AnyStr]) -> AnyStr: ...
    @overload
    def __call__(self, batch: Sequence[Mapping[KT, VT]]) -> Mapping[KT, VT]: ...
    @overload
    def __call__(self, batch: Sequence[Sequence[VT]]) -> Sequence[VT]: ...
fn: _Collate_Fn = default_collate_fn

    def __len__(self):
    def __iter__(self):
    def __call__(self):