帮我写出transformer pytorch版本的代码

好的，我可以为您提供transformer pytorch版本的代码。以下是一个简单的实现：

import torch
import torch.nn as nn

class Transformer(nn.Module):
    def __init__(self, num_layers, d_model, num_heads, d_ff, dropout):
        super().__init__()
        self.encoder_layers = nn.ModuleList([TransformerLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
        self.decoder_layers = nn.ModuleList([TransformerLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
        self.norm = nn.LayerNorm(d_model)
        self.fc_out = nn.Linear(d_model, vocab_size)
        self.embed_src = nn.Embedding(src_vocab_size, d_model)
        self.embed_tgt = nn.Embedding(tgt_vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        self.pos_decoder = PositionalEncoding(d_model, dropout)

    def forward(self, src, tgt, src_mask, tgt_mask):
        src = self.embed_src(src)
        tgt = self.embed_tgt(tgt)
        src = self.pos_encoder(src)
        tgt = self.pos_decoder(tgt)
        for layer in self.encoder_layers:
            src = layer(src, src_mask)
        for layer in self.decoder_layers:
            tgt = layer(tgt, src, tgt_mask, src_mask)
        output = self.fc_out(self.norm(tgt))
        return output

其中，`TransformerLayer`是一个包含注意力机制和前馈网络的模块，`PositionalEncoding`是一个将位置信息嵌入到输入张量中的模块。这个代码可以根据您的具体任务进行修改和扩展。