class ConvNet(nn.Module):
  def __init__(self):
    super(ConvNet, self).__init__()
    self.conv_weight = nn.Parameter(torch.randn(3, 3, 5, 5))
 
  def forward(self, x):
    x = nn.functional.conv2d(x, self.conv_weight, bias=None, stride=1, padding=2, dilation=1, groups=1)
    x = nn.functional.conv2d(x, self.conv_weight.transpose(2, 3).contiguous(), bias=None, stride=1, padding=0, dilation=1,
                 groups=1)
    return x

class LinearNet(nn.Module):
  def __init__(self):
    super(LinearNet, self).__init__()
    self.linear_weight = nn.Parameter(torch.randn(3, 3))
 
  def forward(self, x):
    x = nn.functional.linear(x, self.linear_weight)
    x = nn.functional.linear(x, self.linear_weight.t())
 
    return x

class LinearNet2(nn.Module):
  def __init__(self):
    super(LinearNet2, self).__init__()
    self.w = nn.Parameter(torch.FloatTensor([[1.1,0,0], [0,1,0], [0,0,1]]))
 
  def forward(self, x):
    x = x.mm(self.w)
    x = x.mm(self.w.t())
    return x