refactor: cleanup MLP implementation

[avik-pal]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @avik-pal, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on refactoring the Multi-Layer Perceptron (MLP) implementation to enhance its usability and internal structure. It introduces a more flexible way to define hidden layer dimensions by accepting a vector input, and significantly simplifies the internal block construction by removing an unnecessary wrapper, leading to cleaner and potentially more efficient code.

Highlights

• Version Update: The package version in Project.toml has been incremented from 1.7.0 to 1.7.1.
• MLP Constructor Enhancement: A new constructor for the MLP layer has been added, allowing hidden_dims to be specified as a Vector{<:Integer} for improved flexibility.
• Code Simplification: The DenseNormActDropoutBlock wrapper struct has been removed, and the dense_norm_act_dropout function now directly returns Lux.Dense or Lux.Chain instances, simplifying the internal architecture.

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[gemini-code-assist]

Code Review

This pull request refactors the MLP implementation, which is a good cleanup. The primary changes involve removing the DenseNormActDropoutBlock wrapper for a simpler design that returns Lux layers directly, and adding a new convenience constructor for MLP that accepts a Vector for hidden layer dimensions. These changes improve the code's simplicity and usability. I have one suggestion to further refactor the dense_norm_act_dropout function to improve its conciseness and maintainability.

[avik-pal]

overloaded_map fixed by EnzymeAD/Reactant.jl#1665