Hello, I am trying to learn two distributions with my network. The first one is a categorical distribution and the second one a normal distribution. Below is my pseudocode of the forward() function which illustrates the problem. From input2 i only take the value at that index, which the categorical distribution outputs and then use this value as the mean of the normal distribution. But autograd returns *Nones* since somehow the output is not connected to input. I hope there is a workaround, for which I would be very thankful! The *action_std* is a parameter defined in *init*.

`def forward(input1, input2):`

`x=softmax(layer1(input1))`

`dist1 = Canonical(x) `

`index = dist1.sample() `

`action_mean = input2[index]`

`dist2 = Normal(action_mean, action_std)`

`action = dist2.sample()`

`return action`