Write a custom CUDA kernel to optimize `Nish` (Negative Stimulated Hybrid Activation Function).

Formula:
  f(x) = x                               if x >= 0
  f(x) = sigmoid(x) * (x + sin(x))       if x < 0

Problem Analysis:
1. Memory Bound & Computationally Heavy: The operation is element-wise but involves a complex chain of transcendental functions (exp for sigmoid, sin) for the negative part.
2. Operator Chaining: A PyTorch implementation using `torch.where` would create multiple intermediate tensors (for sigmoid, sin, add), leading to high memory traffic.

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 Branching Logic:
   - For each element `x`, check `if (x < 0)`.
   - If true, compute `sig = 1.0f / (1.0f + __expf(-x))` and `s = __sinf(x)`.
   - Result is `sig * (x + s)`.
   - If false, result is `x`.

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

BATCH_SIZE = 4096
HIDDEN_DIM = 4096
SHAPE = (BATCH_SIZE, HIDDEN_DIM)

class Nish(nn.Module):
    """
    Nish Activation.
    https://arxiv.org/abs/2210.09083v1
    Formula:
      f(x) = x                               if x >= 0
      f(x) = sigmoid(x) * (x + sin(x))       if x < 0
    """
    def __init__(self):
        super(Nish, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        pos_part = x
        neg_part = torch.sigmoid(x) * (x + torch.sin(x))
        return torch.where(x >= 0, pos_part, neg_part)

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.act = Nish()
    
    def forward(self, x):
        return self.act(x)

def get_inputs():
    input_tensor = torch.randn(SHAPE, dtype=torch.float32) * 5.0
    return [input_tensor.contiguous()]

def get_init_inputs():
    return []