将下列自注意力机制的pytorch代码转换为多头自注意力机制的pytorch代码：

以下是将自注意力机制代码转换为多头自注意力机制代码的示例：

import torch
import torch.nn as nn

class SelfAttention(nn.Module):
    def __init__(self, embed_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        
        assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by heads"
        
        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(heads*self.head_dim, embed_size)
        
    def forward(self, values, keys, queries, mask):
        # Get number of training examples
        N = queries.shape[0]
        
        # Split embedding into self.heads pieces
        values = values.reshape(N, -1, self.heads, self.head_dim)
        keys = keys.reshape(N, -1, self.heads, self.head_dim)
        queries = queries.reshape(N, -1, self.heads, self.head_dim)
        
        # Transpose dimensions
        values = values.transpose(1,2) # (N, heads, seq_length, head_dim)
        keys = keys.transpose(1,2) # (N, heads, seq_length, head_dim)
        queries = queries.transpose(1,2) # (N, heads, seq_length, head_dim)
        
        # Calculate the energy
        energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys])
        # queries shape: (N, heads, query_len, head_dim),
        # keys shape: (N, heads, key_len, head_dim)
        # energy shape: (N, heads, query_len, key_len)
        
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float("-1e20"))
        
        # Apply softmax to get attention scores
        attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=3)
        
        # Multiply attention scores with values to get context vectors
        out = torch.einsum("nhql,nlhd->nqhd", [attention, values])
        # attention shape: (N, heads, query_len, key_len)
        # values shape: (N, heads, key_len, head_dim)
        # out shape: (N, query_len, heads, head_dim)
        
        # Concatenate context vectors
        out = out.reshape(N, -1, self.heads*self.head_dim)
        
        # Apply final linear layer
        out = self.fc_out(out)
        
        return out

可以看到，多头自注意力机制的代码在 `__init__` 中添加了用于切分和拼接张量的头数变量 `heads` 和头维度变量 `head_dim`，并在 `forward` 中对输入张量进行分割、转置、矩阵乘法等操作，以实现多头自注意力机制的计算。