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

# 配置：Batch=16, C=64, Length=2048
BATCH = 16
CHANNELS = 64
LENGTH_IN = 2048
KERNEL_SIZE = 3
STRIDE = 2
PADDING = 1

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        # 禁用 cuDNN 以确保确定性结果
        torch.backends.cudnn.enabled = False
        
        # Depthwise Transposed Conv
        self.deconv = nn.ConvTranspose1d(
            in_channels=CHANNELS, 
            out_channels=CHANNELS, 
            kernel_size=KERNEL_SIZE, 
            stride=STRIDE,
            padding=PADDING, 
            groups=CHANNELS, 
            bias=False
        )
        # 权重设为 1.0，消除乘法误差
        nn.init.constant_(self.deconv.weight, 1.0)

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

def get_inputs():
    # 固定随机种子以确保可复现
    torch.manual_seed(42)
    # 使用 [-2, 2] 之间的整数，确保加法精确，且不会溢出 float32
    x = torch.randint(low=-2, high=3, size=(BATCH, CHANNELS, LENGTH_IN), device='cuda').float()
    return [x]

def get_init_inputs():
    return []
```