Hello again, I guess we need to further break down your code to find the error.
first please confirm
The shape of x= (no_samples,116)?
The shape of y=(no_samples,1)?
If this is the case then.



 Mi_Rak:

h1 = th.cat((h, y), dim = 1)


Here in your forward pass, you concat the dimension of FC_1 layer ouput with Y which in my consideration is 1, that makes input for



 Mi_Rak:

h2 = self.hidden(self.relu(self.hidden(h1)))


to be 500+1 for the inner hidden layer.
My suggestion please break this down like this.
h1 = torch.cat((h, y), dim = 1)
#h1 check the output dim after concat.
temp_h1 = self.relu(self.hidden(h1))
#print(temp_h1.size()) to check the output dim.
h2 = self.hidden(temp_h1)