code: def compute_exists_logits(objects, args):
      p_exists = args.expected_num_objects / args.max_num_objects
      real_part = dist.Normal(0.0, 1.0).log_prob(objects)
      real_part = real_part + math.log(p_exists)
      spurious_part = torch.full(
          real_part.shape, math.log(1 - p_exists)
      )
      return real_part - spurious_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 3 for calling a global function with example usage: compute_exists_logits(objects, args) and returns: unknown