There is a set of Pixel Level transforms that is used in the work Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

The authors also share the code => we can absorb some transforms that they have into the library.

https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py

• ShotNoise
• Defocus
• GlassBlur

Describe the bug
I found that some names agruments in framework aren't consistent.
So for example:

class SupervisedRunner(Runner):
    """Runner for experiments with supervised model."""

    _experiment_fn: Callable = SupervisedExperiment

    def __init__(
        self,
        model: Model = None,
        device: Device = None,
        input_key: Any = "features", 
      

Add a way to change the sample id output in the annotation process to a specific number (see picture).

Reason: I want to annotate large text and the app don't like it when the documents to annotate are too large, so I spitted in a sentence the document but I would like to be able to