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Description
Please provide a clear and concise description of the problem. This should contain all the steps nee

Related: awslabs/autogluon#1479

Add a scikit-learn compatible API wrapper of TabularPredictor:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

Discussed in microsoft/FLAML#543

Feature request

As requested by some, and as @ekamioka started on this PR #244. It might be interesting to get some helper functions to use embeddings as it's not the simplest concept in deep learning.

What is the expected behavior?
Calling a few helper function to get all the correct parameters before using TabNet

🐛 Bug

found another paper-cut with instance segmentation...
it is not trivial/intuitive how to match mask with input image as output can be any size and mask is 128x128
so it is just scaled equally in each dimension is applied padding uniformly?

To Reproduce

https://www.kaggle.com/jirkaborovec/cell-instance-segm-lightning-flash#Training-with-Flash-Lightning

Additional cont

Various detectors set torch.device as follows:

       # set backend
        if device is None or device.lower() in ['gpu', 'cuda']:
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
            if self.device.type == 'cpu':
                print('No GPU detected, fall back on CPU.')
        else:
            self.device = torch.device('cpu

Describe the bug
Not necessarily a bug. The dependency of pandas == 1.1.5 is a really obsolete version, (and also many other packages)
For pandas I read through the source and didn't find a version specific usage. Considering change it to pandas >=1.1.5?

🚀 Feature request

The original PyTorch implementation of TabularDropout transformation is available at transformers4rec/torch/tabular/transformations.py