title: forward() takes 1 positional argument but 2 were given
body: <p>I'm trying to build a Model using EfficientNet-B0.
  The details of the Model are shown in the code below.
  I got the following error when I tried to learn.</p>
  
  <pre><code>TypeError                                 Traceback (most recent call last)
  '''
  &lt;ipython-input-17-fb3850894108&gt; in forward(self, *x)
       24         #x: bs*N x 3 x 128 x 128
       25         print(x.shape)     #([384, 3, 224, 224])
  ---&gt; 26         x = self.enc(x)
       27         #x: bs*N x C x 4 x 4
       28         shape = x.shape
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
      548             result = self._slow_forward(*input, **kwargs)
      549         else:
  --&gt; 550             result = self.forward(*input, **kwargs)
      551         for hook in self._forward_hooks.values():
      552             hook_result = hook(self, input, result)
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
       98     def forward(self, input):
       99         for module in self:
  --&gt; 100             input = module(input)
      101         return input
      102 
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
      548             result = self._slow_forward(*input, **kwargs)
      549         else:
  --&gt; 550             result = self.forward(*input, **kwargs)
      551         for hook in self._forward_hooks.values():
      552             hook_result = hook(self, input, result)
  
  TypeError: forward() takes 1 positional argument but 2 were given
  </code></pre>
  
  <p>I suspect that m.children() may have an effect.
  If anyone knows the cause of this error, please let me know.
  Thank you.</p>
  
  <pre><code>class Model(nn.Module):
      def __init__(self, arch='efficientnet-b0', n=6, pre=True):
          super().__init__()
          m = EfficientNet.from_pretrained('efficientnet-b0')
          #print(*list(m.children())
          nc = m._fc.in_features
          print(nc)
          self.enc = nn.Sequential(*list(m.children())[:-2])
          #nc = list(m.children())[-2].in_features
          self.head = nn.Sequential(*list(m.children())[-2:])
          self.head._fc = nn.Linear(nc, n)
          #self.enc = nn.Sequential(*list(m.children()))
          #print('fc_infeatures : {}'.format(nc))
          #self.head = nn.Sequential(Flatten(),nn.Linear(nc,512),
          #                    relu(),nn.BatchNorm1d(512), nn.Dropout(0.5),nn.Linear(512,n))
  
  
      def forward(self, *x):
          print(x[0].shape)  #([32, 3, 224, 224])
          shape = x[0].shape
          n = len(x)           # n = 12
          #torch.stack直後では32*12*3*224*224(bs x N x 3 x 224 x 224)
          x = torch.stack(x,1).view(-1,shape[-3],shape[-2],shape[-1])
          #x: bs*N x 3 x 128 x 128
          print(x.shape)     #([384, 3, 224, 224])
          x = self.enc(x)
          #x: bs*N x C x 4 x 4
          shape = x.shape
          #concatenate the output for tiles into a single map
          x = x.view(-1,n,shape[1],shape[2],shape[3]).permute(0,2,1,3,4).contiguous()\
            .view(-1,shape[1],shape[2]*n,shape[3])
          #x: bs x C x N*4 x 4
          x = self.head(x)
          return x
  
  </code></pre>