给出一个三输入三输出的resnet实例用作数据预测

以下是一个三输入三输出的resnet实例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ThreeInputResNet(nn.Module):
    def __init__(self, input_channels, num_classes, num_filters=64, kernel_size=3, stride=1, padding=3, dilation=1, groups=1, bias=True, padding_mode='zeros'):
        super(ThreeInputResNet, self).__init__()
        self.conv1 = nn.Conv2d(input_channels[0], num_filters, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn1 = nn.BatchNorm2d(num_filters)
        
        self.conv2 = nn.Conv2d(input_channels[1], num_filters, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn2 = nn.BatchNorm2d(num_filters)
        
        self.conv3 = nn.Conv2d(input_channels[2], num_filters, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn3 = nn.BatchNorm2d(num_filters)
        
        self.conv4 = nn.Conv2d(num_filters, num_filters, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn4 = nn.BatchNorm2d(num_filters)
        
        self.conv5 = nn.Conv2d(num_filters, num_filters, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn5 = nn.BatchNorm2d(num_filters)
        
        self.conv6 = nn.Conv2d(num_filters, num_filters, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn6 = nn.BatchNorm2d(num_filters)
        
        self.fc = nn.Linear(num_filters, num_classes)

    def forward(self, x1, x2, x3):
        x1 = self.conv1(x1)
        x1 = self.bn1(x1)
        x1 = F.relu(x1)
        
        x2 = self.conv2(x2)
        x2 = self.bn2(x2)
        x2 = F.relu(x2)
        
        x3 = self.conv3(x3)
        x3 = self.bn3(x3)
        x3 = F.relu(x3)
        
        x = x1 + x2 + x3
        
        x = self.conv4(x)
        x = self.bn4(x)
        x = F.relu(x)
        
        x = self.conv5(x)
        x = self.bn5(x)
        x = F.relu(x)
        
        x = self.conv6(x)
        x = self.bn6(x)
        x = F.relu(x)
        
        x = F.avg_pool2d(x, x.size()[2:])
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        
        return x

这是一个用于三个输入和三个输出的ResNet，其中有6个卷积层和3个批归一化层，以及一个全连接层用于输出预测结果。可以根据需要调整卷积层的参数和结构。