Is it possible to estimate values from following model?

```
def generator(x):
# values to estimate
to_be_need = 3
to_remember = 4
preds = torch.zeros(len(x)).long()
for d in range(len(x)):
# priors
leads = dist.Uniform(100, 200).sample()
# likelihood
needs = dist.Poisson(to_be_need*torch.ones(int(leads))).sample()
memos = dist.Poisson(to_remember*torch.ones(int(leads))).sample()
deals = needs[needs >= memos]
deals = deals[deals < len(x) - d].unique(return_counts=True)
preds[deals[0].long() + d] += deals[1]
return preds
x = torch.arange(0, 100, 1)
y = generator(x)
```

I tried to build naive model, but render shows some problems with dependencies:

```
def model(x, y):
to_be_need = pyro.sample("to_be_need", dist.Uniform(1,7))
to_remember = pyro.sample("to_remember", dist.Uniform(1,7))
sigma = pyro.sample("sigma", dist.Uniform(0, 1))
preds = torch.zeros(len(x)).long()
for d in pyro.plate("days", len(x)):
leads = pyro.sample(f"leads_{d}", dist.Uniform(100, 200))
needs = pyro.sample(f"needs_{d}", dist.Poisson(to_be_need*torch.ones(int(leads))))
memos = pyro.sample(f"memos_{d}", dist.Poisson(to_remember*torch.ones(int(leads))))
deals = Vindex(needs)[needs >= memos]
deals = deals[deals < len(y) - d].unique(return_counts=True)
preds[deals[0].long() + d] += deals[1]
pyro.sample(f"obs_{d}", dist.Normal(preds[d], sigma), obs=y[d])
```

`pyro.render_model(model, model_args=(x, y))`

And inference cause an error:

```
mcmc = MCMC(NUTS(model), num_samples=1000, warmup_steps=200)
mcmc.run(x, y)
```

`ValueError: Continuous inference cannot handle discrete sample site 'needs_0'. Consider enumerating that variable as documented in https://pyro.ai/examples/enumeration.html . If you are already enumerating, take care to hide this site when constructing an autoguide, e.g. guide = AutoNormal(poutine.block(model, hide=['needs_0'])).`

Hint from the error advice to use enumerating, but I have no idea how to apply that