I know I can use gradient descent to infer input [parameters] of a neural network model but for some reason I need to infer those parameters via MCMC. To give you more context, the input has low number of parameters (e.g. 10) and they do not come from a specific distribution (e.g. Gaussian, Uniform etc) but are bounded in [0, 1]. During test time, the neural network model produces an image given an input. My goal is to infer the input parameters conditioned on an output. I am pretty new to Pyro and MCMC but I looked at an example here but cannot figure out how to translate what the example is doing to my problem because `pyro.sample`

seems to require using a specific distribution (maybe I can assume Uniform for my parameters?) and also SVI is used in that example and not MCMC. In addition, the VAE model used in the example has a definition that is different that the model I am using and I’m not sure how to modify the example to make it work in my case. So I wonder, how can I condition my model on an [expected] output and infer the input parameters of my model using Pyro and via MCMC?

Here’s a simplified version of the neural network model I’m using:

```
class model(nn.Module):
def __init__(self):
super(model, self).__init__()
.
. # define model layers
.
def forward(self, x):
.
. # do transposed convolutional operations on x and get predicted_means, predicted_sigma and reconsted_image
.
elbo = torch.sum(Normal(predicted_means, predicted_sigma).log_prob(reconsted_image), dim=[1,2,3])
return elbo
def reconstruct(self, x):
# This is the function I use during test time and MCMC needs to use this function
.
. # do transposed convolutional operations on x and get mu
.
return torch.clamp(mu, 0, 1)
```