Fix incorrect shape issue when loading raw CIFAR-10 data

[f4str]

Description

Remove the lines in art.utils.load_cifar10 which reshape the CIFAR-10 data to a non-standard size.

Fixes #1961

Type of change

Please check all relevant options.

• Improvement (non-breaking)
• Bug fix (non-breaking)
• New feature (non-breaking)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

Testing

Testing is trivial, just load the data and check for the shape.

from art.utils import load_cifar10, load_mnist

(x_train, y_train), (x_test, y_test), min_, max_ = load_cifar10(raw=True)
print(y_train.shape)  # (50000,) instead of (50000, 1)
print(y_test.shape)  # (10000,) instead of (10000, 1)

Test Configuration:

• OS
• Python version
• ART version or commit number
• TensorFlow / Keras / PyTorch / MXNet version

Checklist

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

[codecov-commenter]

Codecov Report

Merging #1962 (00a815b) into dev_1.13.0 (75e37d4) will decrease coverage by 0.01%.
The diff coverage is n/a.

@@              Coverage Diff               @@
##           dev_1.13.0    #1962      +/-   ##
==============================================
- Coverage       85.41%   85.39%   -0.02%     
==============================================
  Files             290      290              
  Lines           25439    25437       -2     
  Branches         4573     4573              
==============================================
- Hits            21728    21722       -6     
- Misses           2542     2544       +2     
- Partials         1169     1171       +2     

Impacted Files	Coverage Δ
art/utils.py	73.30% <ø> (-0.09%)	⬇️
art/estimators/certification/abstain.py	90.90% <0.00%> (-9.10%)	⬇️
art/estimators/poison_mitigation/strip/strip.py	94.44% <0.00%> (-5.56%)	⬇️
art/attacks/evasion/brendel_bethge.py	86.68% <0.00%> (-0.33%)	⬇️

[beat-buesser]

Hi @f4str Thank you very much!