Confusing documentation for argument class_labels in MaskFormerForInstanceSegmentation.forward()
I'm trying to fine-tune MaskFormer for an instance segmentation problem. The documentation for MaskFormerForInstanceSegmentation.forward() lists the following optional parameters:

* mask_labels (List[torch.Tensor], optional) — List of mask labels of shape (num_labels, height, width) to be fed to a model
* class_labels (List[torch.LongTensor], optional) — list of target class labels of shape (num_labels, height, width) to be fed to a model. They identify the labels of mask_labels, e.g. the label of mask_labels[i][j] if class_labels[i][j].

The wording is confusing, especially at the end -- "the label of mask_labels[i][j] if class_labels[i][j]" is missing a verb.

Additionally, other MaskFormer classes in the API accept `class_labels` of shape (labels) - one class label for each mask -  e.g. the forward() method of MaskFormerLoss. It's not clear why this is different in this case.
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