code: def test_full_adapt_sampling(seed=289586):
      np.random.seed(seed)
  
      L = np.random.randn(5, 5)
      L[np.diag_indices_from(L)] = np.exp(L[np.diag_indices_from(L)])
      L[np.triu_indices_from(L, 1)] = 0.0
  
      with pymc.Model() as model:
          pymc.MvNormal("a", mu=np.zeros(len(L)), chol=L, size=len(L))
  
          initial_point = model.initial_point()
          initial_point_size = sum(
              initial_point[n.name].size for n in model.value_vars
          )
  
          with pytest.warns(UserWarning, match="experimental feature"):
              pot = quadpotential.QuadPotentialFullAdapt(
                  initial_point_size, np.zeros(initial_point_size)
              )
          step = pymc.NUTS(model=model, potential=pot)
          with warnings.catch_warnings():
              warnings.filterwarnings(
                  "ignore", ".*number of samples.*", UserWarning
              )
              pymc.sample(
                  draws=10,
                  tune=1000,
                  random_seed=seed,
                  step=step,
                  cores=1,
                  chains=1,
              )
  
imports: import warnings
  import numpy
  import numpy.testing
  import pytest
  import scipy.sparse
  import pymc
  from pymc.pytensorf import floatX
  from pymc.step_methods.hmc import quadpotential
label_desc: Use image node_id 15 for calling a global function with example usage: test_full_adapt_sampling(seed) without return types