对，用python写出

ChitGPT 的代码如下：

import torch
import torch.nn.functional as F

class ChitGPT(torch.nn.Module):
    def __init__(self, vocab_size, embedding_dim, n_head, n_layer, max_len):
        super().__init__()
        
        self.max_len = max_len
        
        # embedding layer
        self.token_embedding = torch.nn.Embedding(vocab_size, embedding_dim)
        
        # transformer layers
        self.transformer_layers = torch.nn.ModuleList([
            torch.nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=n_head)
            for _ in range(n_layer)
        ])
        
        # output layer
        self.fc = torch.nn.Linear(embedding_dim, vocab_size)
        
    def forward(self, x):
        # x has shape (batch_size, sequence_length)
        batch_size, sequence_length = x.shape
        
        # generate mask for padding
        mask = torch.zeros((sequence_length, sequence_length), dtype=torch.bool)
        for i in range(sequence_length):
            mask[i, :i+1] = True
        
        # generate token embeddings
        x = self.token_embedding(x)
        
        # transpose to match transformer input shape
        x = x.permute(1, 0, 2)
        
        # apply transformer layers
        for layer in self.transformer_layers:
            x = layer(x, src_key_padding_mask=mask)
        
        # transpose back to match output shape
        x = x.permute(1, 0, 2)
        
        # apply output layer
        x = self.fc(x)
        
        return x

这是一个基于 Transformer 的语言模型，可以用于生成文本。它包括一个嵌入层、多个 Transformer 编码器层和一个全连接层。在前向传递过程中，它将输入的标记嵌入到低维空间中，然后通过多个 Transformer 编码器层处理输入，最后通过全连接层生成输出。