You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups.   
  
You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.  
  
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

torch.backends.cuda.matmul.allow_tf32 = False

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, xyz: torch.Tensor) -> torch.Tensor:
        xyz = xyz.float()
        dev = xyz.device
        x = xyz[..., 0]
        y = xyz[..., 1]
        z = xyz[..., 2]
        w_rx = torch.tensor( 3.2404542, dtype=torch.float32, device=dev)
        w_ry = torch.tensor(-1.5371385, dtype=torch.float32, device=dev)
        w_rz = torch.tensor(-0.4985314, dtype=torch.float32, device=dev)
        
        w_gx = torch.tensor(-0.9692660, dtype=torch.float32, device=dev)
        w_gy = torch.tensor( 1.8760108, dtype=torch.float32, device=dev)
        w_gz = torch.tensor( 0.0415560, dtype=torch.float32, device=dev)
        
        w_bx = torch.tensor( 0.0556434, dtype=torch.float32, device=dev)
        w_by = torch.tensor(-0.2040259, dtype=torch.float32, device=dev)
        w_bz = torch.tensor( 1.0572252, dtype=torch.float32, device=dev)

        r_step1 = x * w_rx
        r_step2 = y * w_ry
        r_step3 = z * w_rz
        r = (r_step1 + r_step2) + r_step3

        g_step1 = x * w_gx
        g_step2 = y * w_gy
        g_step3 = z * w_gz
        g = (g_step1 + g_step2) + g_step3

        b_step1 = x * w_bx
        b_step2 = y * w_by
        b_step3 = z * w_bz
        b = (b_step1 + b_step2) + b_step3

        return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)

batch_size = 1024 * 1024 
def get_inputs():
    x = torch.rand(batch_size, 3)
    return [x]

def get_init_inputs():
    return []
```