def forward(self, title, content):
        title = self.encoder_tit(title)
        print(title.size())
        title_rehape = torch.index_select(title, 2, Variable(torch.LongTensor([0,2,1])))
        print("title_reshape size is {}".format(title_rehape.size()))

The result is:
torch.Size([1, 3, 64])
title_reshape size is torch.Size([1, 3, 3])

The expected result is [1, 64, 3].Why it’s [1,3,3].I’m confused.
And there is another question emerging.
class Text(nn.Module): 
    def __init__(self):
        super(CNNText, self).__init__()
        self.encoder_tit = nn.Embedding(3281, 64)
        self.encoder_con = nn.Embedding(496037, 512)
        
        self.title_conv_1 = nn.Sequential(
            nn.Conv1d(in_channels = 64,
                      out_channels = 1,
                      kernel_size = (1, 64)),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=1),
        )            
        self.fc = nn.Linear(5, 9)

    def forward(self, title, content):
        title = self.encoder_tit(title)
        print(title.size())
        title_rehape = torch.index_select(title, 2, Variable(torch.LongTensor([0,2,1])))
        print("title_reshape size is {}".format(title_rehape.size()))
        title_out_1 = self.title_conv_1(title_rehape)

The error is:
torch.Size([1, 3, 64])
title_reshape size is torch.Size([1, 3, 3])
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-277-dffba3242fa0> in <module>()
     15     optimizer.zero_grad()
     16 
---> 17     out = cnnt(T, C)
     18     target = cla[epoch]
     19     loss += Loss(out, target)

/home/quoniammm/anaconda3/envs/py3Tfgpu/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

<ipython-input-276-f39b5fc5f928> in forward(self, title, content)
     54         title_rehape = torch.index_select(title, 2, Variable(torch.LongTensor([0,2,1])))
     55         print("title_reshape size is {}".format(title_rehape.size()))
---> 56         title_out_1 = self.title_conv_1(title_rehape)
     57         title_out_2 = self.title_conv_2(title_rehape)
     58         content = self.encoder_con(content)

/home/quoniammm/anaconda3/envs/py3Tfgpu/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

/home/quoniammm/anaconda3/envs/py3Tfgpu/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
     65     def forward(self, input):
     66         for module in self._modules.values():
---> 67             input = module(input)
     68         return input
     69 

/home/quoniammm/anaconda3/envs/py3Tfgpu/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

/home/quoniammm/anaconda3/envs/py3Tfgpu/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input)
    152     def forward(self, input):
    153         return F.conv1d(input, self.weight, self.bias, self.stride,
--> 154                         self.padding, self.dilation, self.groups)
    155 
    156 

/home/quoniammm/anaconda3/envs/py3Tfgpu/lib/python3.6/site-packages/torch/nn/functional.py in conv1d(input, weight, bias, stride, padding, dilation, groups)
     81     f = ConvNd(_single(stride), _single(padding), _single(dilation), False,
     82                _single(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
---> 83     return f(input, weight, bias)
     84 
     85 

RuntimeError: expected 3D tensor

The title_shape has been a 3D tensor.The error makes me very confused.Can you tell me why?
Thanks