code: def test_cholesky_execution(setup):
      rs = np.random.RandomState(0)
      data = rs.randint(1, 10, (10, 10))
      symmetric_data = data.dot(data.T)
  
      a = tensor(symmetric_data, chunk_size=5)
  
      U = cholesky(a)
      t = U.T.dot(U)
  
      res_u = U.execute().fetch()
      np.testing.assert_allclose(np.triu(res_u), res_u)
  
      res = t.execute().fetch()
      np.testing.assert_allclose(res, symmetric_data)
  
      L = cholesky(a, lower=True)
      U = cholesky(a)
      t = L.dot(U)
  
      res = t.execute().fetch()
      np.testing.assert_allclose(res, symmetric_data)
  
      a = tensor(symmetric_data, chunk_size=5)
  
      L = cholesky(a, lower=True)
      U = cholesky(a)
      t = L.dot(U)
  
      res_u = U.execute().fetch()
      np.testing.assert_allclose(np.triu(res_u), res_u)
  
      res = t.execute().fetch()
      np.testing.assert_allclose(res, symmetric_data)
  
      a = tensor(symmetric_data, chunk_size=(2, 3))
  
      L = cholesky(a, lower=True)
      U = cholesky(a)
      t = L.dot(U)
  
      res = t.execute().fetch()
      np.testing.assert_allclose(res, symmetric_data)
  
imports: import numpy
  import scipy.sparse
  from ....learn.datasets.samples_generator import make_low_rank_matrix
  from ....lib.sparse import issparse, SparseNDArray
  from ....utils import ignore_warning
  from ...datasource import tensor, diag, ones, arange
  from ...random import uniform
  from ..None import qr, svd, cholesky, norm, lu, solve_triangular, solve, inv, tensordot, dot, inner, vdot, matmul, randomized_svd
label_desc: Use image node_id 4 for calling a global function with example usage: test_cholesky_execution(setup) without return types