code: def test_norm_execution(setup):
      d = np.arange(9) - 4
      d2 = d.reshape(3, 3)
  
      ma = [tensor(d, chunk_size=2), tensor(d2, chunk_size=(2, 3))]
  
      for i, a in enumerate(ma):
          data = d if i < 1 else d2
          for ord in (None, "nuc", np.inf, -np.inf, 0, 1, -1, 2, -2):
              for axis in (0, 1, (0, 1), -1):
                  for keepdims in (True, False):
                      try:
                          expected = np.linalg.norm(
                              data,
                              ord=ord,
                              axis=axis,
                              keepdims=keepdims,
                          )
                          t = norm(
                              a, ord=ord, axis=axis, keepdims=keepdims
                          )
                          res = t.execute().fetch()
  
                          expected_shape = expected.shape
                          t_shape = t.shape
                          assert expected_shape == t_shape
  
                          np.testing.assert_allclose(
                              res, expected, atol=0.0001
                          )
                      except ValueError:
                          continue
  
      m = norm(tensor(d))
      expected = m.execute().fetch()
      res = np.linalg.norm(d)
      assert expected == res
  
      d = uniform(-0.5, 0.5, size=(5000, 2), chunk_size=1000)
      inside = (norm(d, axis=1) < 0.5).sum().astype(float)
      t = inside / 5000 * 4
      res = t.execute().fetch()
      np.testing.assert_almost_equal(3.14, res, decimal=1)
  
      raw = np.random.RandomState(0).rand(10, 10)
      d = norm(tensor(raw, chunk_size=5))
      expected = d.execute().fetch()
      result = np.linalg.norm(raw)
      np.testing.assert_allclose(expected, result)
  
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 10 for calling a global function with example usage: test_norm_execution(setup) without return types