Write a custom CUDA kernel to optimize `SCL Mish` (Soft Clipping Mish learnable).

Formula: f(x) = max(0, x * tanh(softplus(alpha * x)))
where softplus(z) = log(1 + exp(z)).

Problem Analysis:
1. Computationally Intensive & Memory Bound: The operation is element-wise but involves a long chain of transcendental functions (exp, log, tanh).
2. Operator Chaining: A standard PyTorch implementation creates multiple intermediate tensors and kernel launches.

Optimization Strategy: Fused Element-wise Kernel with Vectorization

1. One-Thread-per-Element: Map each element to a CUDA thread.

2. Vectorized Loads (float4): Use `float4` to process 128 bits per memory transaction.

3. Fused Stable Math:
   - For each element `x`, compute `ax = alpha * x`.
   - Compute stable softplus: `sp = (ax > 20) ? ax : log1pf(__expf(ax))`.
   - Compute `mish_part = x * tanhf(sp)`.
   - Result `fmaxf(mish_part, 0.0f)`.
   - All steps are fused in registers.

4. One-Pass: Fuse all steps into a single read-compute-write kernel.
  
Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:   
  
```python
import torch
import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(self, a, b):
        return a + b


def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]


def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []