Drop a malformed transform from StackedTransform automatically

[vinnamkim]

Summary

• Ticket no. 106054
• A malformed transform can be stacked on _StackTransform. Therefore, it would fail if the user tries to apply several transforms to the dataset sequentially and there exist malformed transforms. Also, once a malformed transform is given to Dataset.transform(), that transform permanently remains in the dataset, so creating a new dataset is the only way to solve it.
• This patch pops up the malformed transform from the transform stack automatically if it raises an error at construction.

How to test

Unit tests are added for this change.

Checklist

• I have added unit tests to cover my changes.​
• I have added integration tests to cover my changes.​
• I have added the description of my changes into CHANGELOG.​
• I have updated the documentation accordingly

License

• I submit my code changes under the same MIT 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: MIT

[codecov-commenter]

Codecov Report

Patch coverage: 100.00% and project coverage change: +0.01 🎉

Comparison is base (3cd5f4d) 78.52% compared to head (763817c) 78.53%.

Additional details and impacted files

@@             Coverage Diff             @@
##           develop    #1001      +/-   ##
===========================================
+ Coverage    78.52%   78.53%   +0.01%     
===========================================
  Files          233      233              
  Lines        26757    26770      +13     
  Branches      5323     5325       +2     
===========================================
+ Hits         21012    21025      +13     
  Misses        4496     4496              
  Partials      1249     1249              

Flag	Coverage Δ
macos-11_Python-3.8	77.54% <100.00%> (+0.01%)	⬆️
ubuntu-20.04_Python-3.8	78.52% <100.00%> (?)
windows-2019_Python-3.8	78.42% <100.00%> (+0.01%)	⬆️

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Impacted Files	Coverage Δ
datumaro/components/dataset.py	82.75% <100.00%> (+0.33%)	⬆️

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[sooahleex]

Looks good to me

[bonhunko]

LGTM

[wonjuleee]

It looks good to me for avoiding an error, but it may require to remove the root cause of an error when stacking transforms. Is there any way to resolve this?

[wonjuleee]

It looks good to me for avoiding an error, but it may require to remove the root cause of an error when stacking transforms. Is there any way to resolve this?