TuringLang / JuliaBUGS.jl / 17583314660

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Add an argument to `condition` function to skip generating and evaling log density function (#394)

## Motivation
`condition` currently regenerates the log-density function on every
call, invoking MacroTools transforms and `eval`. This is too slow for
hot loops.

We want a fast path for repeated conditioning that:
- Avoids regeneration
- Lets users update observed values in-place without changing graph
structure
- Allows explicit regeneration later to regain compiled performance when
desired

## Summary of Changes
- `condition` now supports a fast path via a keyword flag:
- `condition(model, conditioning_spec;
regenerate_log_density::Bool=true)`
- When `false`: skips regeneration, nulls the compiled function, and
forces graph evaluation mode
- New helper for value updates without reconditioning:
  - `set_observed_values!(model, obs::Dict{<:VarName,<:Any})`
- Updates evaluation environment values of already-observed stochastic
variables in-place
  - Validates existence, stochasticity, and observation status
- New function to regenerate the compiled log-density function without
changing the evaluation mode:
  - `regenerate_log_density_function(model; force=false)`
- Re-generates for the model’s current graph + environment and refreshes
graph evaluation data
- Exports added:
- `set_observed_values!` and `regenerate_log_density_function` (from
`Model` module)

## Tiny Benchmark for Sanity Check
with 
```julia
@bugs begin
    for i in 1:N
        x[i] ~ Normal(0, 1)
    end
    y ~ Normal(sum(x[:]), 1)
end
```

```
BenchmarkTools results (N=100, iters=200):
  Single condition: regular = 13.96 ms, fast = 0.15 ms, speedup ≈ 92.3x
  Loop conditioning: regular = 28139.5 ms, fast(update only) = 0.2 ms, speedup ≈ 112840.1x
```

Interpretation:
- Single call to `condition`: ~90x faster when skipping regeneration
- Hot loop: one fast `condition` + repeated `set_observed_values!` is
~1e5x faster than repeatedly reconditioning

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