File E:\abcd\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs) 1126 # If we don't have any hooks, we want to skip the rest of the logic in 1127 # this function, and just call forward. 1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1129 or _global_forward_hooks or _global_forward_pre_hooks): -> 1130 return forward_call(*input, **kwargs) 1131 # Do not call functions when jit is used 1132 full_backward_hooks, non_full_backward_hooks = [], [] Cell In[29], line 10, in RNN.forward(self, x) 8 def forward(self, x): 9 h0 = torch.zeros(1, x.size(0), self.hidden_size) ---> 10 out, _ = self.rnn(x, h0) 11 out = self.fc(out[:, -1, :]) 12 return out File E:\abcd\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs) 1126 # If we don't have any hooks, we want to skip the rest of the logic in 1127 # this function, and just call forward. 1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1129 or _global_forward_hooks or _global_forward_pre_hooks): -> 1130 return forward_call(*input, **kwargs) 1131 # Do not call functions when jit is used 1132 full_backward_hooks, non_full_backward_hooks = [], [] File E:\abcd\lib\site-packages\torch\nn\modules\rnn.py:445, in RNN.forward(self, input, hx) 443 if hx is not None: 444 if hx.dim() != 2: --> 445 raise RuntimeError( 446 f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor") 447 hx = hx.unsqueeze(1) 448 else: RuntimeError: For unbatched 2-D input, hx should also be 2-D but got 3-D tensor是什么问题，该如何解决？

这是一个PyTorch中的运行时错误信息，提示在使用RNN模型时，输入的初始隐藏状态（hx）的维度不正确。具体来说，当输入x是一个未进行批处理的二维张量时，初始隐藏状态hx的维度也应该是2，但实际上hx的维度是3，因此会抛出此错误。解决这个问题的方法是，确保输入和初始隐藏状态的维度匹配。可以通过调整输入张量的形状或初始隐藏状态张量的形状来解决这个问题，使它们的维度都是二维的。例如，可以使用`unsqueeze()`方法将初始隐藏状态的维度从2扩展到3。