Introduce AdaptiveRepeatDataHook for the classification

[sungmanc]

Summary

This PR introduces the AdaptiveRepeatDataHook and OTXSampler for the classification now, later I will consider it for other tasks. It seems that it's not huge work that applies to other tasks.

By changing the number of iterations per epoch, we could significantly reduce the training time at the small data regime.
For the classification, we could check the improvements by checking the table below.

Number of training data: 16
Number of repeats: 5

mode	performance	gpu_mem	iter_time	data_time	e2e_time
develop	0.870	3081	0.380	0.312	0:00:17
sampler	0.881	3084	0.215 (-43.3%)	0.147 (-53.1%)	0:00:13 (-25.4%)

When we considered to use the AdaptiveRepeatDataHook, AdaptiveSchedulingHook should be disabled. So, I changed the default value at the configuration.yaml

This commit(fb40fea) added to resolve the issue(#2427)

For now, this sampler only affects to the multi-class classification.

How to test

Checklist

• I have added unit tests to cover my changes.​
• I have added integration tests to cover my changes.​
• I have added e2e tests for validation.
• I have added the description of my changes into CHANGELOG in my target branch (e.g., CHANGELOG in develop).​
• I have updated the documentation in my target branch accordingly (e.g., documentation in develop).
• I have linked related issues.

License

• I submit my code changes under the same Apache License that covers the project.
  Feel free to contact the maintainers if that's a concern.
• I have updated the license header for each file (see an example below).

# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

[eunwoosh]

Thanks for your work! It can reduce much overhead, I think. I left some comments, please take a look.

[sungmanc]

Thanks for your work! It can reduce much overhead, I think. I left some comments, please take a look.

Thanks for the detailed review, it helps a lot :), I finished to reflect your review

[eunwoosh]

LGTM. Thanks for applying lots of comment :)

[jaegukhyun]

LGTM, I have one comment

[goodsong81]

Thank you for the update. I have a few questions.