Write a custom CUDA kernel to optimize `Serf` (Log-Softplus Error Activation Function).

Formula: f(x) = x * erf(ln(1 + exp(x)))
This is equivalently: x * erf(softplus(x))

Problem Analysis:
1. Computationally Expensive: The `erf` (Error Function) combined with `log` and `exp` creates a heavy arithmetic load per element.
2. Memory Bottleneck: Executing `softplus` then `erf` then `mul` creates multiple intermediate global memory accesses.

Optimization Strategy: Fused Element-wise Kernel with Vectorization

1. One-Pass Fused Kernel: Compute the entire Serf function in a single kernel pass. Read `x`, compute `result`, write `result`.

2. Vectorized Loads (float4): Use `float4` to load 128 bits per thread instruction. This maximizes memory bandwidth utilization.

3. Robust Softplus Implementation:
   - Compute `sp = softplus(x)`. Use `x > 20 ? x : log1p(exp(x))` for numerical stability.
   - Then compute `erf(sp)` using CUDA's fast intrinsic `erff` or standard `erf` (depending on precision needs, standard is preferred for activation).
   - Finally `x * erf_val`.

4. Fast Math: Use fast math compiler flags (`-use_fast_math` or `__expf`) to speed up the transcendental chain.
  
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
import torch.nn.functional as F

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

class Serf(nn.Module):
    """
    Serf Activation: x * erf(ln(1 + e^x))
    https://arxiv.org/pdf/2108.09598
    """
    def __init__(self):
        super(Serf, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        sp = F.softplus(x)
        e = torch.erf(sp)
        return x * e

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

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

def get_init_inputs():
    return []