引用transformer的encoder结构写一个encoder+lstm的代码

对于一个encoder lstm，其结构类似于transformer的encoder，但具体实现方式有所不同。以下是一个可能的encoder lstm的代码实现：

import torch
import torch.nn as nn

class EncoderLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(EncoderLSTM, 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)

    def forward(self, x):
        # x shape: (batch_size, seq_len, input_size)
        # h0 shape: (num_layers, batch_size, hidden_size)
        # c0 shape: (num_layers, batch_size, hidden_size)

        # Initialize hidden and cell states
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)

        # Forward propagate LSTM
        out, (hidden, cell) = self.lstm(x, (h0, c0))

        # Return output and final hidden and cell states
        # out shape: (batch_size, seq_len, hidden_size)
        # hidden shape: (num_layers, batch_size, hidden_size)
        # cell shape: (num_layers, batch_size, hidden_size)
        return out, hidden, cell

这个encoder lstm的代码实现包括一个LSTM层，其输入为`x`，输出为`out`，`hidden`和`cell`。其中，`x`的shape为`(batch_size, seq_len, input_size)`，`hidden`和`cell`的shape为`(num_layers, batch_size, hidden_size)`。在forward方法中，我们首先初始化了hidden和cell状态，然后将x和初始化的hidden和cell状态传入LSTM层进行前向传播，最后返回LSTM层的输出和最终的hidden和cell状态。