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

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

Problem Analysis:
The standard PyTorch implementation involves a chain of element-wise operations: exponential, hyperbolic tangent, and multiplication.
1. exp(x) creates an intermediate tensor.
2. tanh(intermediate) creates another intermediate tensor.
3. x * result creates the final output.
This chain results in excessive global memory read/write traffic, making the operation memory-bound. Additionally, computing two transcendental functions (exp, tanh) per element creates high arithmetic pressure.

Optimization Strategy: Fused Element-wise Kernel with Vectorized Access and Fast Math

1. Operator Fusion: Create a single CUDA kernel that computes `x * tanh(exp(x))` in one pass. Each thread reads `x` once into a register, computes the entire mathematical expression, and writes the result back. This minimizes global memory accesses.

2. Vectorized Memory Access: Use `float4` types to load and store 128 bits (4 floats) per instruction. This drastically improves memory bandwidth utilization and reduces instruction overhead.

3. Grid-Stride Loop: Implement the kernel using a grid-stride loop pattern. This ensures the kernel works correctly and efficiently for input tensors of any size, decoupling the grid configuration from the specific data size.

4. Fast Math Intrinsics: Since the kernel involves `exp` and `tanh`, utilizing fast math intrinsics (like `__expf` or compiling with `--use_fast_math`) is crucial to reduce the latency of the ALU operations, allowing them to be effectively hidden by the optimized memory access.

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 TanhExp(nn.Module):
    """
    公式: f(x) = x * tanh(e^x)
    """
    def __init__(self):
        super(TanhExp, self).__init__()

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

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

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

def get_init_inputs():
    return []