nn.LSTM的源码是什么

nn.LSTM是PyTorch中的一个预定义模型，其源代码实现可以在PyTorch的GitHub仓库中找到。以下是nn.LSTM的源代码实现：

class LSTM(RNNBase):
    def __init__(self, *args, **kwargs):
        super(LSTM, self).__init__('LSTM', *args, **kwargs)

    def check_forward_args(self, input, hidden, batch_sizes):
        self.check_input(input, batch_sizes)
        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)

        def check_hidden_size(hx, expected_hidden_size, msg='Expected hidden size {}, got {}'):
            if tuple(hx.size()) != expected_hidden_size:
                raise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))

        if self.mode == 'LSTM':
            check_hidden_size(hidden[0], expected_hidden_size, 'Expected hidden[0] size {}, got {}')
            check_hidden_size(hidden[1], expected_hidden_size, 'Expected hidden[1] size {}, got {}')
        else:
            check_hidden_size(hidden, expected_hidden_size)

    @weak_script_method
    def forward(self, input, hx=None):
        is_packed = isinstance(input, PackedSequence)
        if is_packed:
            input, batch_sizes = input.data, input.batch_sizes
            max_batch_size = int(batch_sizes[0])
        else:
            batch_sizes = None
            max_batch_size = input.size(0) if self.batch_first else input.size(1)

        if hx is None:
            num_directions = 2 if self.bidirectional else 1
            hx = input.new_zeros(self.num_layers * num_directions,
                                 max_batch_size, self.hidden_size,
                                 requires_grad=False)
            hx = self._init_hidden(hx)

        self.check_forward_args(input, hx, batch_sizes)
        func = self._backend.RNN_backward if self.mode == 'RNN' else self._backend.LSTM_backward
        output, hidden = func(
            input, hx, self.all_weights, self.bias, self.num_layers,
            self.dropout, self.training, self.bidirectional, self.batch_first)

        if is_packed:
            output = PackedSequence(output, batch_sizes)
        return output, hidden

以上代码实现了LSTM的前向传播过程。其中，对于输入数据进行Packed操作的部分用于支持变长序列输入的情况。