I have a (conditional) normalizing flow model very similar to the one outlined in the Normalizing Flows - Introduction (Part 1) tutorial. I am trying to train the model multiple times to assess its stability and take an average of its results. I am trying to use a loop to do this, but I am having issues resetting the parameters for each retraining:

```
modules = torch.nn.ModuleList([transform_1, transform_2]).to(device)
optimizer = torch.optim.Adam(modules.parameters(), lr=lr)
steps = 201
bootstraps = 10
for bootstrap in range(bootstraps):
pyro.get_param_store().clear()
for step in range(steps):
optimizer.zero_grad()
# log liklihood
ln_p_1 = dist_1.log_prob(data_1)
ln_p_2_given_1 = dist_2_given_1.condition(data_1).log_prob(data_2)
...
loss_train.backward()
optimizer.step()
dist_1.clear_cache()
dist_2_given_1.clear_cache()
```

which gives:

```
bootstrap: 0 step: 0, train loss: 11.535920143127441
bootstrap: 0 step: 200, train loss: 4.8237104415893555
bootstrap: 1 step: 0, train loss: 4.820072174072266
bootstrap: 1 step: 200, train loss: 4.592116832733154
bootstrap: 2 step: 0, train loss: 4.59176778793335
...
```

The model keeps training using the same parameters (essentially training only one model). Here, `transform_1`

is a `SplineAutoregressive`

transform and `transform_2`

is a `ConditionalSplineAutoregressive`

transform.

I’d appreciate if anyone could guide me on how to resolve this issue.

Thanks!