x.view(x.size(0), -1)

a = torch.Tensor(2,3)
print(a)
# tensor([[0.0000, 0.0000, 0.0000],
#    [0.0000, 0.0000, 0.0000]])
 
print(a.view(1,-1))
# tensor([[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])

def forward(self,x):
  x=self.pre(x)
  x=self.layer1(x)
  x=self.layer2(x)
  x=self.layer3(x)
  x=self.layer4(x)
    
  x=F.avg_pool2d(x,7)
  x=x.view(x.size(0),-1)
  return self.fc(x)

tensor_name.view(shape)

 > x = torch.randn(4, 4)
 > x.size()
 torch.Size([4, 4])
 > y = x.view(16)
 > y.size()
 torch.Size([16])

> z = x.view(-1, 8) # the size -1 is inferred from other dimensions
> z.size()
torch.Size([2, 8])

 > y = x.view(-1)
 > y.size()
 torch.Size([16])

> a = torch.randn(1, 2, 3, 4)
> a.size()
torch.Size([1, 2, 3, 4])
> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension
> b.size()
torch.Size([1, 3, 2, 4])
> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory
> c.size()
torch.Size([1, 3, 2, 4])
> torch.equal(b, c)
False