我们可以使用PyTorch实现Attention机制，并设置不同的层大小。具体步骤如下：

1.首先，我们需要定义一个encoder类来处理输入数据，这里我们使用一个简单的BiLSTM模型来演示：

import torch.nn as nn

class Encoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(Encoder, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
        
    def forward(self, input):
        # input shape: (batch_size, seq_len, input_size)
        h0 = torch.zeros(self.num_layers*2, input.size(0), self.hidden_size).to(device) # initialize hidden state to 0
        c0 = torch.zeros(self.num_layers*2, input.size(0), self.hidden_size).to(device) # initialize cell state to 0
        output, (hidden, cell) = self.lstm(input, (h0, c0)) # output shape: (batch_size, seq_len, hidden_size*2)
        return hidden, cell

2.接下来，我们定义一个Attention类，该类接受encoder的输出作为输入，并返回加权后的encoder输出：

class Attention(nn.Module):
    def __init__(self, hidden_size, attention_size):
        super(Attention, self).__init__()
        self.hidden_size = hidden_size
        self.attention_size = attention_size
        self.fc1 = nn.Linear(hidden_size*2, attention_size, bias=False)
        self.fc2 = nn.Linear(attention_size, 1, bias=False)
        
    def forward(self, encoder_output, last_hidden):
        # encoder_output shape: (batch_size, seq_len, hidden_size*2)
        # last_hidden shape: (num_layers*2, batch_size, hidden_size)
        last_hidden.unsqueeze_(1) # last_hidden shape: (num_layers*2, 1, batch_size, hidden_size)
        last_hidden = last_hidden.repeat(1, encoder_output.size(1), 1, 1) # last_hidden shape: (num_layers*2, seq_len, batch_size, hidden_size)
        encoder_output = encoder_output.permute(2, 0, 1) # encoder_output shape: (hidden_szie*2, batch_size, seq_len)
        energy = torch.tanh(self.fc1(encoder_output) + self.fc1(last_hidden)) # energy shape: (attention_size, batch_size, seq_len)
        attention