Method __call__

monai/transforms/croppad/array.py:682–690

Apply the transform to `img`, assuming `img` is channel-first and slicing doesn't apply to the channel dim.

(self, img: torch.Tensor, randomize: bool = True, lazy: bool | None = None)

        super().randomize(img_size)

    def __call__(self, img: torch.Tensor, randomize: bool = True, lazy: bool | None = None) -> torch.Tensor:  # type: ignore
        """
        Apply the transform to `img`, assuming `img` is channel-first and
        slicing doesn't apply to the channel dim.

        """
        self.get_max_roi_size(img.peek_pending_shape() if isinstance(img, MetaTensor) else img.shape[1:])
        lazy_ = self.lazy if lazy is None else lazy
        return super().__call__(img=img, randomize=randomize, lazy=lazy_)


class RandSpatialCropSamples(Randomizable, TraceableTransform, LazyTransform, MultiSampleTrait):