能否提供一个完整的可运行代码（不是片段，而是能够在变长循环神经网络上运行的代码），展示如何使用PyTorch中的PackedSequence方法？

在文档、GitHub或网络上似乎找不到这方面的示例。

https://github.com/pytorch/pytorch/releases/tag/v0.1.10

回答：

这段代码并不是最优雅的，但这是我在研究PyTorch论坛和文档后为个人使用而整理出来的。处理排序和恢复部分肯定有更好的方法，但我选择在网络本身中进行处理。

编辑：请查看来自@tusonggao的回答，他使用torch utils来处理排序部分

class Encoder(nn.Module):    def __init__(self, vocab_size, embedding_size, embedding_vectors=None, tune_embeddings=True, use_gru=True,                 hidden_size=128, num_layers=1, bidrectional=True, dropout=0.6):        super(Encoder, self).__init__()        self.embed = nn.Embedding(vocab_size, embedding_size, padding_idx=0)        self.embed.weight.requires_grad = tune_embeddings        if embedding_vectors is not None:            assert embedding_vectors.shape[0] == vocab_size and embedding_vectors.shape[1] == embedding_size            self.embed.weight = nn.Parameter(torch.FloatTensor(embedding_vectors))        cell = nn.GRU if use_gru else nn.LSTM        self.rnn = cell(input_size=embedding_size, hidden_size=hidden_size, num_layers=num_layers,                        batch_first=True, bidirectional=True, dropout=dropout)    def forward(self, x, x_lengths):        sorted_seq_lens, original_ordering = torch.sort(torch.LongTensor(x_lengths), dim=0, descending=True)        ex = self.embed(x[original_ordering])        pack = torch.nn.utils.rnn.pack_padded_sequence(ex, sorted_seq_lens.tolist(), batch_first=True)        out, _ = self.rnn(pack)        unpacked, unpacked_len = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)        indices = Variable(torch.LongTensor(np.array(unpacked_len) - 1).view(-1, 1)                                                                       .expand(unpacked.size(0), unpacked.size(2))                                                                       .unsqueeze(1))        last_encoded_states = unpacked.gather(dim=1, index=indices).squeeze(dim=1)        scatter_indices = Variable(original_ordering.view(-1, 1).expand_as(last_encoded_states))        encoded_reordered = last_encoded_states.clone().scatter_(dim=0, index=scatter_indices, src=last_encoded_states)        return encoded_reordered