Write a custom CUDA kernel to optimize `TeLU` (Hyperbolic Tangent Exponential Linear Unit).

Formula: f(x) = x * tanh(exp(x))

Problem Analysis:
1. Memory Bound & Computationally Heavy: The operation is element-wise but involves a chain of transcendental functions (exp, tanh).
2. Operator Chaining: A PyTorch implementation `x * torch.tanh(torch.exp(x))` creates intermediate tensors for `exp` and `tanh`, 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)`
     `tanh_val = tanhf(exp_val)`
     `result = x * tanh_val`
   - 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 TeLU(nn.Module):
    """
    TeLU Activation: f(x) = x * tanh(exp(x))
    https://arxiv.org/abs/2412.20269
    """
    def __init__(self):
        super(TeLU, 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 = TeLU()
    
    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 []