Write a custom CUDA kernel to optimize the LiSHT activation function.

The mathematical definition is:
f(x) = x * tanh(x)

Problem Analysis:
The standard PyTorch implementation involves two element-wise operations: `tanh` and `multiplication`.
1. tanh(x) computes the hyperbolic tangent, creating an intermediate tensor in global memory.
2. x * intermediate multiplies the input by the result.
This results in two reads (one for x, one for intermediate) and one write (final result) for the second operation, plus the read/write for the first operation. This is inefficient due to excessive memory traffic.

Optimization Strategy: Fused Element-wise Kernel with Vectorized Access

1. Operator Fusion: Create a single CUDA kernel that computes `x * tanh(x)` in one pass. Each thread reads `x` once into a register, computes the result, and writes it back. This reduces global memory transactions to the theoretical minimum (1 read, 1 write).

2. Vectorized Memory Access: Use `float4` types to load and store 128 bits (4 floats) per instruction. This increases memory throughput and instruction efficiency by processing 4 elements per thread.

3. Grid-Stride Loop: Use a grid-stride loop pattern to ensure the kernel can handle input tensors of arbitrary size robustly, decoupling the grid size from the data size.

4. Fast Math Intrinsics: Utilizing `tanhf` (or fast math options) within the kernel ensures that the calculation latency does not become a bottleneck compared to memory bandwidth.
  
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
DIM = 4096
SHAPE = (BATCH_SIZE, DIM)

class LiSHT(nn.Module):
    """
    公式: f(x) = x * tanh(x)
    """
    def __init__(self):
        super(LiSHT, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x * torch.tanh(x)

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.lisht = LiSHT()
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.lisht(x)

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

def get_init_inputs():
    return []