This is probably a very basic question, but here I go anyway.

I’m trying to create a DeepNet of a series of Linear layers. Here is a class called ‘NeuralNet’:

```
class NeuralNet(nn.Module):
def __init__(self, weights, bias):
super(NeuralNet, self).__init__()
self.weights = weights
self.bias = bias
self.nn = nn.Sequential(
nn.Linear(10, 10),
nn.ReLU(inplace = True),
nn.Linear(10, 10),
nn.ReLU(inplace = True),
nn.Linear(10, 10),
nn.ReLU(inplace = True),
nn.Linear(10, 3),
nn.ReLU(inplace = True),
)
def forward(self, a, b, c):
a = torch.flatten(a)
b = torch.flatten(b)
c = torch.flatten(c)
print(a.shape, b.shape, c.shape)
a1 = self.nn(a)
b1 = self.nn(b)
c1 = self.nn(c)
a1 = self.nn(s1)
b1 = self.nn(b1)
c1 = self.nn(ep1)
a1 = self.nn(a1)
b1 = self.nn(b1)
c1 = self.nn(c1)
a1 = self.nn(a1)
b1 = self.nn(b1)
c1 = self.nn(c1)
y = torch.cat((a1, b1, c1))
return y
```

The randomized tensors a, b, c all have a shape of (10, 1).