写一个四层的一维卷积代码，包含了relu和pooling，输入为batchsize=4，且向量为1280维度

以下是一个四层的一维卷积代码，包含了relu和pooling，输入为batchsize=4，且向量为1280维度的示例代码：

import torch
import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv1d(1280, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.layer3 = nn.Sequential(
            nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.layer4 = nn.Sequential(
            nn.Conv1d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.fc = nn.Linear(512, 10)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out

model = ConvNet()
print(model)

这个示例代码中，我们使用了 PyTorch 框架来实现了一个四层的一维卷积神经网络，其中包含了 relu 和 pooling 操作。输入的张量大小为 (batch_size, 1280)，输出的张量大小为 (batch_size, 10)。