Refactor DCGAN training script to use Flux.setup/update! unified API

[josemanuel22]

Motivation and description

Description:
The current train.jl script manually manages optimizer state and uses the low-level Optimisers.jl API (Optimisers.setup/Optimisers.update! or explicit params(model) loops). We should refactor it to leverage Flux's unified Flux.setup and Flux.update! API introduced in Flux 0.13+, for a cleaner and more idiomatic training loop.

Current Behavior:

• Optimizers are initialized and updated using the explicit Optimisers.jl API:

  opt = ADAM(lr)
  ps  = params(model)
  st  = Optimisers.setup(opt, ps)
  ...
  st, _ = Optimisers.update!(st, ps, grads)

• Training loops manually handle gradient collection and state updates.

Desired Behavior:

• Use Flux's unified optimizer API:

  opt_state = Flux.setup(Flux.Optimise.Adam(lr), model)
  
  # inside training loop
  loss, grads = Flux.withgradient(model) do m
    loss_fn(m(x))
  end
  Flux.update!(opt_state, model, grads[1])

• Simplify train_discriminator! and train_generator! to use Flux.setup/Flux.update! instead of explicit parameter/state management.

Proposed Changes:

1. Replace all explicit Optimisers.setup and Optimisers.update! calls with Flux.setup and Flux.update! on the Chain models.
2. Remove manual params(model) and state-tracking variables where no longer needed.

Acceptance Criteria:

• Code compiles and runs without deprecation warnings under Flux 0.14+.
• Discriminator and generator training functions use Flux.setup and Flux.update! exclusively.
• Existing functionality and performance are preserved.

References:

• Flux unified training API: https://fluxml.ai/Flux.jl/stable/guide/training/training/