Add MC-Dropout uncertainty estimation for patch classification

[Pier45]

Adds Monte Carlo Dropout-based uncertainty quantification (epistemic + aleatoric)
for patch classification models.

TIAToolbox's pretrained classification models (AlexNet, MobileNet, etc.) don't
expose uncertainty estimates, only point predictions. This adds a lightweight,
non-invasive wrapper to enable MC-Dropout sampling and uncertainty decomposition.

• bayesian_wrapper.py: wraps any model, injects dropout at inference if needed
• mc_dropout.py: runs N stochastic forward passes
• uncertainty.py: computes epistemic/aleatoric/total uncertainty
• Includes unit tests

[codecov]

Codecov Report

❌ Patch coverage is 78.82353% with 18 lines in your changes missing coverage. Please review.
✅ Project coverage is 99.73%. Comparing base (9a27bbb) to head (d644b7b).

Files with missing lines	Patch %	Lines
tiatoolbox/models/architecture/bayesian_wrapper.py	72.09%	11 Missing and 1 partial ⚠️
tiatoolbox/models/engine/mc_dropout.py	81.81%	4 Missing and 2 partials ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##           develop    #1104      +/-   ##
===========================================
- Coverage    99.88%   99.73%   -0.16%     
===========================================
  Files           86       89       +3     
  Lines        11670    11755      +85     
  Branches      1533     1544      +11     
===========================================
+ Hits         11657    11724      +67     
- Misses           7       22      +15     
- Partials         6        9       +3     

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