TODO

Core:
- [ ] Enable `Grid` to represent a batch of sampling grids, or add `GridBatch` (cf. `Image` <-> `ImageBatch`).

Data:
- [x] Implement `Image.split`, `ImageBatch.split`, `FlowField.split`, and `FlowFields.split` such that return value is of same Tensor subclass type with adjusted spatial grid in case of a split along a spatial image dimension.

Loss functions:
- [x] Normalized mutual information.

Data transforms:
- [ ] Basic data augmentations.

Spatial transforms:
- [x] Fix __repr__ of transforms with "params" set to a torch.nn.Module.
- [x] More efficient implementation of cubic B-spline evaluation than using transposed convolutions.

Utility functions:
- [ ] Function to save `FreeFormTransformation` parameters in MIRTK `.dof.gz` format.

Neural networks:
- [x] Follow MONAI and rename `dimensions` argument of `__init__` functions to `spatial_dims`.

Examples:
- Traditional free-form deformation algorithm:
  - [x] Write example code and script for pairwise image registration using deepali.
  - [ ] Restrict registration to foreground region of interest, especially when segmentations are given.
  - [ ] Enable and demonstrate parallel registration of multiple pairs / source images in single batch.
- Image-and-Spatial Transformer Networks:
  - [ ] Write training script and test with synthetic examples used in paper.
  - [ ] Write test-time refinement script.

Tests:
- [ ] Full test coverage of core library functions.
- [ ] Tests of spatial transformation models.
- [ ] Tests for main data classes.
- [ ] Tests for datasets.
- [ ] Tests of loss terms.

Documentation:
- [x] Add Sphinx based `docs`, see `docs/source/conf.py` of PyTorch or kornia.