可以写一段多尺度特征提取模块的代码吗

当涉及到图像处理时，多尺度特征提取是一项非常重要的任务。以下是一个基本的多尺度特征提取模块的代码示例，该模块使用了卷积和池化操作：

import torch.nn as nn

class MultiScaleFeatureExtractor(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(MultiScaleFeatureExtractor, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        
    def forward(self, x):
        # 1st convolution and pooling
        out1 = self.conv1(x)
        out1 = self.pool1(out1)
        
        # 2nd convolution and pooling
        out2 = self.conv2(out1)
        out2 = self.pool2(out2)
        
        # 3rd convolution
        out3 = self.conv3(out2)
        
        # concatenate all outputs
        out = torch.cat((out1, out2, out3), dim=1)
        
        return out

该模块接受输入张量 `x`，并在不同的尺度上执行卷积和池化操作，然后将所有输出连接在一起返回。这个模块可以嵌入到更大的深度学习模型中，以提取多尺度特征。