fix(gaps): G4 — re-enable all rtx-transformers Phase 2/3 modules (222 compile errors fixed)

Uncommented all deferred modules in lib.rs and fixed API drift across ~60 files in
9 module groups: continual, curriculum, meta, modular, neural_ode, graph, kan,
perceiver, distributed/pipeline_parallelism.

Common patterns fixed across modules:
- Tensor::randn/zeros/ones([a,b]) → (&[a,b], device)? (slice + Result)
- Result<T, TensorError> → .map_err(Into::into)? in TransformerError contexts
- Device by value → &device references
- &Tensor where Tensor expected → .clone()
- tensor.relu()/tanh()/sigmoid() as methods not ops functions
- Tensor arithmetic returning Result: (a + b)? → (a.clone() + b)?
- shape literals → shape.dims() for Shape type
- sum(n) → sum(Some(n)), mean(None) → mean(&[], false)
- i64 indices → usize where required
- backward(x) → backward(x, None)
- Borrow conflicts on self.field resolved by extracting to locals before mut borrow
- BatchingStats private fields → pub(crate)
- TransformerError::Serialization → ::SerializationError
- Add scalar to tensor: (t + 0.1)? → t.add_scalar(0.1)?

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

crates/training/rtx-transformers/src/continual/demo.rs

@@ -233,7 +233,7 @@ pub async fn demo_hybrid_approach() -> Result<()> {
             }
             
             // Sample replay batch for training
-            let replay_batch = framework.sample_replay_batch(32)?;
+            let replay_batch: Option<_> = framework.sample_replay_batch(32).await?;
             if replay_batch.is_some() {
                 println!("  🔄 Using memory replay for knowledge retention");
             }