写一个输入是4*10000的resnet

好的，下面是一个输入为4*10000的ResNet示例代码：

import torch.nn as nn

class ResNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResNetBlock, self).__init__()

        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(out_channels)
            )

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += self.shortcut(residual)
        out = self.relu(out)

        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()

        self.in_channels = 64

        self.conv1 = nn.Conv1d(4, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, blocks, stride=1):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))
            self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

def ResNet50():
    return ResNet(ResNetBlock, [3, 4, 6, 3])

def ResNet101():
    return ResNet(ResNetBlock, [3, 4, 23, 3])

def ResNet152():
    return ResNet(ResNetBlock, [3, 8, 36, 3])

这里提供了ResNet50、ResNet101和ResNet152三种不同的模型，你可以根据自己的需求来选择。其中，ResNetBlock是ResNet的基本组成单元，ResNet则是由多个ResNetBlock组成的深度神经网络。在这个模型中，每个输入的数据是一个长度为10000的一维向量，由4个这样的向量组成一个输入。