写一个将列表长度为3作为输入的AlexNet结合branchynet的代码

以下是一个将列表长度为3作为输入的AlexNet结合branchynet的代码的示例：

import torch
import torch.nn as nn
from branchynet import BranchyNet

class AlexNetWithBranchy(nn.Module):
    def __init__(self):
        super(AlexNetWithBranchy, self).__init__()
        
        # Define the main AlexNet model
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 1000),
        )
        
        # Define the branchy network branches
        self.branch1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.branch2 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(32, 64, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 96, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(96, 96, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(96, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.branch3 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )

        # Define the branchy network
        self.branchy_net = BranchyNet(self.features, [self.branch1, self.branch2, self.branch3], self.classifier)
        
    def forward(self, x):
        # Check that input is a list of length 3
        if not isinstance(x, list) or len(x) != 3:
            raise ValueError("Input must be a list of length 3")
        
        # Pass input through branchy network
        out = self.branchy_net(x)
        
        return out

在这个示例中，我们首先定义了主要的AlexNet模型，然后定义了三个分支，每个分支都是一个类似于AlexNet的卷积神经网络。然后，我们使用`BranchyNet`类将这些分支和主要模型组合在一起。最后，我们重写了`forward`方法，以便它可以处理输入列表的长度为3。

请注意，这只是一个示例，您可能需要根据您的具体需求进行修改。