Implement random_state for IBIS8542Model MLP

[ConorMacBride]

A random_state integer can be passed as a kwarg to IBIS8542Model, and applied to the default MLP classifier (if the default classifier is being used). This allows for the randomness of the training process to be controlled, and means that a neural network trained with the same data and code will produce the same trained network every time, so allows for reproducibility. The Working with IBIS data gallery example has also been updated to use a persistent neural network and explain how to create a reproducible classifier.

[codecov]

Codecov Report

Merging #33 (6d9fde0) into main (3e42120) will increase coverage by 0.16%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main      #33      +/-   ##
==========================================
+ Coverage   94.33%   94.49%   +0.16%     
==========================================
  Files          16       16              
  Lines        1217     1217              
==========================================
+ Hits         1148     1150       +2     
+ Misses         69       67       -2     

Impacted Files	Coverage Δ
src/mcalf/models/ibis.py	91.77% <100.00%> (+0.63%)	⬆️
src/mcalf/models/base.py	84.78% <0.00%> (+0.31%)	⬆️

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