Set TabularTransform to process clean transform in parallel

[sooahleex]

Summary

• Clean transform took long time due to tabular dataset usually have large dataset
• To reduce the time for transform, I made TabularTransform to do this process in parallel
• When I set batch_size as 100 and num_workers as 2, then the total process time reduces in half (about 300s -> 166s)

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 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) 2024 Intel Corporation
#
# SPDX-License-Identifier: MIT

[codecov]

Codecov Report

Attention: Patch coverage is 93.75000% with 2 lines in your changes missing coverage. Please review.

Please upload report for BASE (releases/1.10.0@9478b9a). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
src/datumaro/components/transformer.py	93.10%	1 Missing and 1 partial ⚠️

Additional details and impacted files

@@                Coverage Diff                 @@
##             releases/1.10.0    #1648   +/-   ##
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  Coverage                   ?   81.12%           
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  Files                      ?      283           
  Lines                      ?    32861           
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  Hits                       ?    26659           
  Misses                     ?     4751           
  Partials                   ?     1451           

Flag	Coverage Δ
ubuntu-20.04_Python-3.10	81.10% <93.75%> (?)
windows-2022_Python-3.10	81.10% <93.75%> (?)

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