Write a custom CUDA kernel to optimize `torch.conj_physical` for complex tensors.

The operation computes the element-wise conjugate of a complex tensor. For z = x + iy, conj_physical(z) = x - iy. It explicitly materializes the result in memory.

Problem Analysis:
This is a strictly memory-bound operation.
1. Data Layout: `complex64` stores data as contiguous pairs of floats [Real, Imag].
2. Computation: The only arithmetic operation is negating the imaginary part.
3. Bottleneck: The performance is strictly limited by Global Memory bandwidth.

Optimization Strategy: Vectorized Access (2x Complex Elements per Thread)

1. Vectorized I/O (Float4): 
   - A single `complex64` is 8 bytes (2 floats).
   - Using `float4` (16 bytes) allows a single thread to load/store **two** complex numbers at once.
   - Layout loaded into registers: `x`=Real1, `y`=Imag1, `z`=Real2, `w`=Imag2.

2. In-Register Computation:
   - Negate the `y` and `w` components (the imaginary parts).
   - Store the modified `float4` back to global memory.

3. Grid-Stride Loop: Implement a robust grid-stride loop to handle arbitrary tensor sizes, processing 2 complex elements per iteration in the vectorized loop, and handling remainders with a scalar loop.
  
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 ConjPhysicalModel(nn.Module):
    def __init__(self):
        super(ConjPhysicalModel, self).__init__()

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

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

def get_inputs():
    x = torch.randn(SHAPE, dtype=torch.complex64)
    return [x.contiguous()]

def get_init_inputs():
    return []