帮我写一个基于transformer的图像分类代码，附上注释

好的，以下是基于Transformer的图像分类代码，注释在代码中：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

# 定义Transformer Encoder层
class TransformerEncoderLayer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1):
        super(TransformerEncoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, src, src_mask=None, src_key_padding_mask=None):
        # Multi-Head Attention
        src2 = self.self_attn(src, src, src, attn_mask=src_mask,
                              key_padding_mask=src_key_padding_mask)[0]
        # Layer Normalization
        src = self.norm1(src + self.dropout1(src2))
        # Feed Forward Network
        src2 = self.linear2(self.dropout(F.relu(self.linear1(src))))
        # Dropout
        src = self.norm2(src + self.dropout2(src2))
        return src

# 定义Transformer Encoder模块
class TransformerEncoder(nn.Module):
    def __init__(self, encoder_layer, num_layers, norm=None):
        super(TransformerEncoder, self).__init__()
        self.layers = nn.ModuleList([encoder_layer for i in range(num_layers)])
        self.num_layers = num_layers
        self.norm = norm

    def forward(self, src, mask=None, src_key_padding_mask=None):
        output = src

        for layer in self.layers:
            output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask)

        if self.norm is not None:
            output = self.norm(output)

        return output

# 定义Transformer模型
class TransformerModel(nn.Module):
    def __init__(self, d_model, nhead, num_layers, num_classes):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Linear(224 * 224 * 3, d_model)
        self.encoder = TransformerEncoder(TransformerEncoderLayer(d_model, nhead), num_layers)
        self.fc = nn.Linear(d_model, num_classes)

    def forward(self, x):
        # 将图像展开成一维向量
        x = x.view(x.size(0), -1)
        # 进行embedding
        x = self.embedding(x)
        # Transformer Encoder
        x = self.encoder(x)
        # 全连接层
        x = self.fc(x[:, 0, :])
        return x

这里的代码是一个简单的图像分类模型，使用Transformer Encoder来提取图像特征，并使用全连接层来进行分类。需要注意的是，这里的输入是224x224x3大小的图像，经过线性变换之后变成了一维向量，因此需要将图像展开成一维向量再进行embedding。