get_batch() called torch.nn.functional.interpolate on a mismatched
noise mask but discarded the result, so the mask kept its original
size. The height check was also missing the *8 pixel scale. When a
rebatched latent carried a noise mask that did not match samples * 8
(e.g. from SetLatentNoiseMask, which stores masks unscaled), the
unresized mask later failed to concatenate with another latent's
default mask and raised a size-mismatch RuntimeError.
Assign the interpolate result back to mask and compare the height
against shape[-2] * 8.
* Move dataset/text nodes to text category
* Rename category utils into utilities
* Rename category api node into partner
* Move categories conditioning, latent, sampling, model_patches, training, etc. under model category
* Dispatch partner nodes in to 3d, audio, image, text, video categories
* Move PreviewAny node to utilities category
* allow nodes to map over lists
* make work with IS_CHANGED and VALIDATE_INPUTS
* give list outputs distinct socket shape
* add rebatch node
* add batch index logic
* add repeat latent batch
* deal with noise mask edge cases in latentfrombatch
