code: def test_logistic_regression(jit, use_multinomial_sampling):
      dim = 3
      data = torch.randn(2000, dim)
      true_coefs = torch.arange(1.0, dim + 1.0)
      labels = dist.Bernoulli(
          logits=(true_coefs * data).sum(-1)
      ).sample()
  
      def model(data):
          coefs_mean = torch.zeros(dim)
          coefs = pyro.sample(
              "beta", dist.Normal(coefs_mean, torch.ones(dim))
          )
          y = pyro.sample(
              "y",
              dist.Bernoulli(logits=(coefs * data).sum(-1)),
              obs=labels,
          )
          return y
  
      nuts_kernel = NUTS(
          model,
          use_multinomial_sampling=use_multinomial_sampling,
          jit_compile=jit,
          ignore_jit_warnings=True,
      )
      mcmc = MCMC(nuts_kernel, num_samples=500, warmup_steps=100)
      mcmc.run(data)
      samples = mcmc.get_samples()
      assert_equal(
          rmse(true_coefs, samples["beta"].mean(0)).item(),
          0.0,
          prec=0.1,
      )
  
imports: import logging
  import os
  from collections import namedtuple
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.optim
  import pyro.poutine
  from pyro.contrib.conjugate.infer import BetaBinomialPair, GammaPoissonPair, collapse_conjugate, posterior_replay
  from pyro.infer import SVI, TraceEnum_ELBO
  from pyro.infer.autoguide import AutoDelta
  from pyro.infer.mcmc import MCMC, NUTS, ArrowheadMassMatrix
  from pyro.util import ignore_jit_warnings
  from tests.common import assert_close, assert_equal
  from .test_hmc import GaussianChain, rmse
label_desc: Use image node_id 4 for calling a global function with example usage: test_logistic_regression(jit, use_multinomial_sampling) and returns: y