Write a custom CUDA kernel to optimize the `modSwish` activation function.

Formula: f(x) = x / (1 - x * exp(-x))

Problem Analysis:
1. Computationally Intensive & Memory Bound: The operation is element-wise but involves an exponential function, multiplication, subtraction, and division.
2. Operator Chaining: A PyTorch implementation creates intermediate tensors for `exp`, `mul`, etc., wasting memory bandwidth.

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 In-Register Math:
   - For each element `x`:
     `exp_val = __expf(-x)`
     `denom = 1.0f - x * exp_val`
     `result = x / denom`
   - All computations 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

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

class ModSwish(nn.Module):
    """
    modSwish: a new activation function for neural network
    https://link.springer.com/article/10.1007/s12065-024-00908-9

    Formula: f(x) = x / (1 - x * exp(-x))
    """
    def __init__(self):
        super(ModSwish, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x / (1.0 - x * torch.exp(-x))

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.act = ModSwish()
    
    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 []