帮我用pytorch框架复现mobilenetV2，要求：1.源代码2.写好注释文档

好的，以下是使用 PyTorch 框架复现 MobileNet V2 的代码和注释文档。

import torch
import torch.nn as nn

class Block(nn.Module):
    '''Depthwise convolutional block.'''
    def __init__(self, in_channels, out_channels, stride=1, expansion=6):
        super(Block, self).__init__()
        self.stride = stride
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.use_residual = self.stride == 1 and self.in_channels == self.out_channels
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels, in_channels * expansion, kernel_size=1, bias=False),
            nn.BatchNorm2d(in_channels * expansion),
            nn.ReLU6(inplace=True)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels * expansion, in_channels * expansion, kernel_size=3, stride=stride, padding=1, groups=in_channels * expansion, bias=False),
            nn.BatchNorm2d(in_channels * expansion),
            nn.ReLU6(inplace=True)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(in_channels * expansion, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels),
        )
        
    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        if self.use_residual:
            out += residual
        return out

class MobileNetV2(nn.Module):
    '''MobileNetV2 implementation.'''
    def __init__(self, num_classes=1000, width_mult=1.0):
        super(MobileNetV2, self).__init__()
        self.num_classes = num_classes
        
        # Define the network architecture
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU6(inplace=True),
            
            Block(32, 16, stride=1, expansion=1),
            Block(16, 24, stride=2, expansion=6),
            Block(24, 24, stride=1, expansion=6),
            Block(24, 32, stride=2, expansion=6),
            Block(32, 32, stride=1, expansion=6),
            Block(32, 32, stride=1, expansion=6),
            Block(32, 64, stride