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
import torch.nn.functional as F

BATCH = 64
CHANNELS = 512
LENGTH = 4096
BLOCK_SIZE = 7
KEEP_PROB = 0.9

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.block_size = BLOCK_SIZE
        self.keep_prob = KEEP_PROB
        self.gamma = None

    def calculate_gamma(self, x):
        return (1.0 - self.keep_prob) / self.block_size * \
               x.shape[-1] / (x.shape[-1] - self.block_size + 1)

    def forward(self, x: torch.Tensor, rand_tensor: torch.Tensor) -> torch.Tensor:
        if self.gamma is None:
            self.gamma = self.calculate_gamma(x)

        mask = (rand_tensor < self.gamma).float()

        pad = self.block_size // 2
        mask = F.max_pool1d(mask, kernel_size=self.block_size, stride=1, padding=pad)

        mask = 1.0 - mask
      
        scale = 1.0 / self.keep_prob
        
        return x * mask * scale

def get_inputs():
    x = torch.randn(BATCH, CHANNELS, LENGTH, device='cuda', dtype=torch.float32)
    rand = torch.rand_like(x)
    return [x, rand]

def get_init_inputs():
    return []
```