可以使用Keras中的MultiHeadAttention层来实现多头注意力机制。具体步骤如下：
1. 导入MultiHeadAttention层：`from tensorflow.keras.layers import MultiHeadAttention`
2. 在conv1out和conv2out之后分别添加一个MultiHeadAttention层，代码如下：
```
attn1 = MultiHeadAttention(num_heads=8, key_dim=64)
attn1_out = attn1(gru1out, gru1out)
attn1_out = Dropout(config.dropout)(attn1_out)
attn2 = MultiHeadAttention(num_heads=8, key_dim=64)
attn2_out = attn2(gru2out, gru2out)
attn2_out = Dropout(config.dropout)(attn2_out)
```
其中，num_heads表示头的数量，key_dim表示每个头的维度。
3. 将attn1_out和attn2_out拼接起来，代码如下：
```
attn_out = concatenate([attn1_out, attn2_out])
```
4. 将attn_out作为输入，继续进行后续操作，代码如下：
```
output = Dense(trainY.shape[1])(attn_out)
```
完整代码如下：
```
from tensorflow.keras.layers import MultiHeadAttention
conv1out = conv1(input1)
gru1out = CuDNNGRU(64)(conv1out)
gru1out = Dropout(config.dropout)(gru1out)
attn1 = MultiHeadAttention(num_heads=8, key_dim=64)
attn1_out = attn1(gru1out, gru1out)
attn1_out = Dropout(config.dropout)(attn1_out)
conv2out = conv2(input2)
gru2out = CuDNNGRU(64)(conv2out)
gru2out = Dropout(config.dropout)(gru2out)
attn2 = MultiHeadAttention(num_heads=8, key_dim=64)
attn2_out = attn2(gru2out, gru2out)
attn2_out = Dropout(config.dropout)(attn2_out)
attn_out = concatenate([attn1_out, attn2_out])
output = Dense(trainY.shape[1])(attn_out)
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