code: def step_continuous(self, step_fn, draws, chains=1, tune=1000):
      start, model, (mu, C) = mv_simple()
      unc = np.diag(C) ** 0.5
      check = (
          ("x", np.mean, mu, unc / 10),
          ("x", np.std, unc, unc / 10),
      )
      _, model_coarse, _ = mv_simple_coarse()
      with model:
          step = step_fn(C, model_coarse)
          with warnings.catch_warnings():
              warnings.filterwarnings(
                  "ignore", "More chains .* than draws .*", UserWarning
              )
              idata = pm.sample(
                  tune=tune,
                  draws=draws,
                  chains=chains,
                  step=step,
                  initvals=start,
                  model=model,
                  random_seed=1,
                  discard_tuned_samples=False,
              )
          assert idata.warmup_posterior.sizes["chain"] == chains
          assert idata.warmup_posterior.sizes["draw"] == tune
          assert idata.posterior.sizes["chain"] == chains
          assert idata.posterior.sizes["draw"] == draws
          self.check_stat(check, idata)
          self.check_stat_dtype(idata, step)
  
imports: import contextlib
  import shutil
  import tempfile
  import warnings
  from logging.handlers import BufferingHandler
  import numpy
  import numpy.random
  import pytensor
  from numpy.testing import assert_array_less
  from pytensor.gradient import verify_grad
  import pymc
  from pymc.testing import fast_unstable_sampling_mode
  from tests.models import mv_simple, mv_simple_coarse
label_desc: Use image node_id 5 for calling the StepMethodTester obj's underlying member method code with example usage: obj.step_continuous(step_fn, draws, chains, tune) without return types