Operator: Tanh-Affine-Gate (Fused CUDA Kernel)

Goal
- Fuse affine, tanh activation, sigmoid gate, and multiply for fewer memory passes and kernel overhead, targeting ≥1.30x speedup.

Inputs/Outputs
- Input `x`: [B, D], float32
- Parameters `scale`, `bias`: [D], float32
- Scalars `alpha`, `beta`: float32
- Output `y`: [B, D], float32

Definition
- z = x * scale + bias
- m = tanh(z)
- g = sigmoid(alpha * m + beta)
- y = x * g

CUDA Design
- 2D grid; block=256; ILP=2; float4 vectorization
- FMA for affine, fast math for tanh/sigmoid
- Pass scalars (`alpha`, `beta`) as plain doubles to avoid sync

Validation
- Accuracy: torch.allclose(rtol=1e-3)
- Performance: ≥1.30x speedup at B=16, D=16384
