code: def test_mv_missing_data_model(self):
      data = ma.masked_values(
          [[1, 2], [2, 2], [-1, 4], [2, -1], [-1, -1]], value=-1
      )
  
      model = pm.Model()
      with model:
          mu = pm.Normal("mu", 0, 1, size=2)
          sd_dist = pm.HalfNormal.dist(1.0, size=2)
          chol, *_ = pm.LKJCholeskyCov(
              "chol_cov", n=2, eta=1, sd_dist=sd_dist
          )
          with pytest.warns(ImputationWarning):
              y = pm.MvNormal("y", mu=mu, chol=chol, observed=data)
          inference_data = pm.sample(
              tune=10,
              draws=10,
              chains=2,
              step=pm.Metropolis(),
              idata_kwargs=dict(log_likelihood=True),
          )
  
      # make sure that data is really missing
      assert isinstance(
          y.owner.inputs[0].owner.op,
          (AdvancedIncSubtensor, AdvancedIncSubtensor1),
      )
  
      test_dict = {
          "posterior": ["mu", "chol_cov"],
          "observed_data": ["y_observed"],
          "log_likelihood": ["y_observed"],
      }
      fails = check_multiple_attrs(test_dict, inference_data)
      assert not fails
  
imports: import warnings
  from typing import Dict, Tuple
  import numpy
  import pytensor.tensor
  import pytest
  from arviz import InferenceData
  from arviz.tests.helpers import check_multiple_attrs
  from numpy import ma
  from pytensor.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1
  import pymc
  from pymc.backends.arviz import InferenceDataConverter, predictions_to_inference_data, to_inference_data
  from pymc.exceptions import ImputationWarning
label_desc: Use image node_id 13 for calling the TestDataPyMC obj's underlying member method code with example usage: obj.test_mv_missing_data_model() without return types