code: def test_sample_mvn(full_cov: bool) -> None:
      """
      Draws 10,000 samples from a distribution
      with known mean and covariance. The test checks
      if the mean and covariance of the samples is
      close to the true mean and covariance.
      """
      N, D = 10000, 2
      means = tf.ones((N, D), dtype=float_type)
      if full_cov:
          covs = tf.eye(D, batch_shape=[N], dtype=float_type)
      else:
          covs = tf.ones((N, D), dtype=float_type)
  
      samples = sample_mvn(means, covs, full_cov)
      samples_mean = np.mean(samples, axis=0)
      samples_cov = np.cov(samples, rowvar=False)
  
      np.testing.assert_array_almost_equal(
          samples_mean, [1.0, 1.0], decimal=1
      )
      np.testing.assert_array_almost_equal(
          samples_cov, [[1.0, 0.0], [0.0, 1.0]], decimal=1
      )
  
imports: from typing import Callable, List, Sequence, Tuple, cast
  import numpy
  import pytest
  import scipy
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import ShapeChecker, check_shapes
  import gpflow
  import gpflow.inducing_variables.multioutput
  import gpflow.kernels.multioutput
  from gpflow import set_trainable
  from gpflow.base import AnyNDArray, RegressionData
  from gpflow.conditionals import sample_conditional
  from gpflow.conditionals.util import fully_correlated_conditional, fully_correlated_conditional_repeat, independent_interdomain_conditional, sample_mvn
  from gpflow.config import default_float, default_jitter
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import SquaredExponential
  from gpflow.likelihoods import Gaussian
  from gpflow.models import SVGP
label_desc: Use image node_id 6 for calling a global function with example usage: test_sample_mvn(full_cov) without return types