Usuage with torchgeo and kornia

[Geethen]

I was initially struggling to figure out how to use flipnslide with torchgeo data loaders and transforms powered by kornia. Please see the attached reproducible example for any interested. This might be a useful addition to the docs

import torch
from flipnslide.tiling import FlipnSlide
from typing import Any
from torch import Tensor
import kornia as K

class _FlipnSlide(K.augmentation.GeometricAugmentationBase2D):
    """Flip and slide a tensor."""

    def __init__(self, tilesize: int, viz: bool = False) -> None:
        """Initialize a new _FlipnSlide instance.

        Args:
            tilesize: desired tile size
            viz: visualization flag
        """
        super().__init__(same_on_batch = True )
        self.flags = {'tilesize': tilesize, 'viz': viz}

    def compute_transformation(
        self, input: Tensor, params: dict[str, Tensor], flags: dict[str, Any]
    ) -> Tensor:
        """Compute the transformation.

        Args:
            input: the input tensor
            params: generated parameters
            flags: static parameters

        Returns:
            the transformation
        """
        
        out: Tensor = self.identity_matrix(input)
        return out

    def apply_transform(
        self,
        input: Tensor,
        params: dict[str, Tensor],
        flags: dict[str, Any],
        transform: Tensor = None,
    ) -> Tensor:
        """Apply the transform.

        Args:
            input: the input tensor
            params: generated parameters
            flags: static parameters
            transform: the geometric transformation tensor

        Returns:
            the augmented input
        """
        np_array = input.squeeze(0).cpu().numpy()
        sample_tiled = FlipnSlide(
            tile_size=flags['tilesize'], 
            data_type='tensor',
            save=False, 
            image=np_array,
            viz=flags['viz']
        )
        return sample_tiled.tiles

# Usage with AugmentationSequential
flipnslide = _FlipnSlide(tilesize=64, viz=False)
tfms = AugmentationSequential(
    flipnslide,
    data_keys=None
)
# Example usage
train_batch = {
    'image': torch.rand(1, 3, 256, 256),
    'mask': torch.rand(1, 1, 256, 256)
}

transformed = tfms(train_batch)

print("Transformed image shape:", transformed['image'].shape)
print("Transformed mask shape:", transformed['mask'].shape)
print(transformed)