帮我基于pytorch框架构建一个结构，要求一个输入分别经过三个liner层，然后三个输出相加输入至一个多头注意力机制层，最后输出至一个liner层然后输出

好的，以下是基于PyTorch框架构建的模型结构：

import torch.nn as nn
import torch.nn.functional as F

class MyModel(nn.Module):
    def __init__(self, input_dim, output_dim, num_heads):
        super(MyModel, self).__init__()
        
        self.linear1 = nn.Linear(input_dim, input_dim)
        self.linear2 = nn.Linear(input_dim, input_dim)
        self.linear3 = nn.Linear(input_dim, input_dim)
        
        self.multihead_attn = nn.MultiheadAttention(input_dim, num_heads)
        
        self.linear4 = nn.Linear(input_dim, output_dim)
        
    def forward(self, x):
        x1 = F.relu(self.linear1(x))
        x2 = F.relu(self.linear2(x))
        x3 = F.relu(self.linear3(x))
        
        x = x1 + x2 + x3
        
        x = x.permute(1, 0, 2) # change to shape [seq_len, batch_size, input_dim]
        x, _ = self.multihead_attn(x, x, x)
        x = x.permute(1, 0, 2) # change back to shape [batch_size, seq_len, input_dim]
        
        x = self.linear4(x)
        
        return x

这个模型接收一个维度为`input_dim`的输入，经过三个线性层后，将三个输出相加，然后输入到一个多头注意力机制层中，该层的头数为`num_heads`。最后，输出将输入到一个线性层中，输出维度为`output_dim`。