code: def generate_data(args):
      num_objects = args.expected_num_objects
      true_objects = torch.randn(num_objects)
      true_assign = dist.Categorical(
          torch.ones(args.num_real_detections, num_objects)
      ).sample()
      real_detections = true_objects[true_assign]
      real_detections = (
          real_detections
          + args.init_noise_scale * torch.randn(real_detections.shape)
      )
      fake_detections = torch.randn(args.num_fake_detections)
      detections = torch.cat([real_detections, fake_detections])
      assert detections.shape == (
          args.num_real_detections + args.num_fake_detections,
      )
      return 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 6 for calling a global function with example usage: generate_data(args) and returns: detections