code: def compute_assign_logits(objects, detections, noise_scale, args):
      num_detections = len(detections)
      p_fake = args.num_fake_detections / num_detections
      real_part = dist.Normal(objects, noise_scale).log_prob(detections)
      real_part = real_part + math.log(
          (1 - p_fake) / args.max_num_objects
      )
      fake_part = dist.Normal(0.0, 1.0).log_prob(detections)
      fake_part = fake_part + math.log(p_fake)
      return real_part - fake_part
  
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 4 for calling a global function with example usage: compute_assign_logits(objects, detections, noise_scale, args) and returns: unknown