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

The mathematical definition is:
f(x) = x * tanh(x * GELU(x))
where GELU(x) = 0.5 * x * (1 + erf(x / sqrt(2)))

Problem Analysis:
The Phish activation function represents a "deeply nested" composite operator. The standard PyTorch implementation is highly inefficient because:
1. Memory Bandwidth: It involves a chain of operations (GELU, Multiply, Tanh, Multiply). Each step reads from and writes to global memory, creating significant intermediate tensor overhead.
2. Arithmetic Intensity: It requires calculating two transcendental functions per element (`erf` inside GELU, and `tanh` outside). In a non-fused implementation, the latency of these instructions exposes memory latency even more.

Optimization Strategy: Fused Element-wise Kernel with Vectorized Access

1. Operator Fusion: Create a single CUDA kernel that evaluates the entire `x * tanh(x * GELU(x))` expression in one pass. Each thread loads input `x` once into a register, performs all arithmetic (including the nested GELU and Tanh), and writes the final result. This reduces global memory accesses to the theoretical minimum.

2. Vectorized Memory Access: Use `float4` types to load/store 128 bits per instruction. This is crucial for hiding the latency of the expensive `erf` and `tanh` instructions by ensuring the memory pipeline is fully saturated.

3. Numerical Implementation: Use CUDA intrinsic `erff` for the GELU calculation and `tanhf` for the outer shell. 

4. Grid-Stride Loop: Implement the kernel using a grid-stride loop pattern to support arbitrary input tensor sizes robustly.
  
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
DIM = 4096
SHAPE = (BATCH_SIZE, DIM)

class Phish(nn.Module):
    """
    Phish Activation Function.
    https://vixra.org/pdf/2112.0097v3.pdf
    Formula: f(x) = x * tanh(x * GELU(x))
    """
    def __init__(self):
        super(Phish, self).__init__()

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

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