code: def model(detections, args):
      noise_scale = pyro.param("noise_scale")
      objects = pyro.param("objects_loc").squeeze(-1)
      (num_detections,) = detections.shape
      (max_num_objects,) = objects.shape
  
      # Existence part.
      p_exists = args.expected_num_objects / max_num_objects
      with pyro.plate("objects_plate", max_num_objects):
          exists = pyro.sample("exists", dist.Bernoulli(p_exists))
          with poutine.mask(mask=exists.bool()):
              pyro.sample("objects", dist.Normal(0.0, 1.0), obs=objects)
  
      # Assignment part.
      p_fake = args.num_fake_detections / num_detections
      with pyro.plate("detections_plate", num_detections):
          assign_probs = torch.empty(max_num_objects + 1)
          assign_probs[:-1] = (1 - p_fake) / max_num_objects
          assign_probs[-1] = p_fake
          assign = pyro.sample(
              "assign", dist.Categorical(logits=assign_probs)
          )
          is_fake = assign == assign.shape[-1] - 1
          objects_plus_bogus = torch.zeros(max_num_objects + 1)
          objects_plus_bogus[:max_num_objects] = objects
          real_dist = dist.Normal(
              objects_plus_bogus[assign], noise_scale
          )
          fake_dist = dist.Normal(0.0, 1.0)
          pyro.sample(
              "detections",
              dist.MaskedMixture(is_fake, real_dist, fake_dist),
              obs=detections,
          )
  
imports: import logging
  import math
  import pytest
  import torch
  from torch.distributions import constraints
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.contrib.tracking.assignment import MarginalAssignment
  from pyro.infer import SVI, TraceEnum_ELBO
  from pyro.optim import Adam
  from pyro.optim.multi import MixedMultiOptimizer, Newton
label_desc: Use image node_id 2 for calling a global function with example usage: model(detections, args) without return types