code: def test_svd_execution(setup):
      rs = np.random.RandomState()
      data = rs.randn(18, 6) + 1j * rs.randn(18, 6)
  
      a = tensor(data, chunk_size=(9, 6))
      U, s, V = svd(a)
      t = U.dot(diag(s).dot(V))
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      a = tensor(data, chunk_size=(18, 6))
      U, s, V = svd(a)
      t = U.dot(diag(s).dot(V))
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      a = tensor(data, chunk_size=(2, 6))
      U, s, V = svd(a)
      t = U.dot(diag(s).dot(V))
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      data = rs.randn(6, 18) + 1j * rs.randn(6, 18)
  
      a = tensor(data)
      U, s, V = svd(a)
      t = U.dot(diag(s).dot(V))
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      # test for matrix of ones
      data = np.ones((20, 10))
  
      a = tensor(data, chunk_size=10)
      s = svd(a)[1]
      res = s.execute().fetch()
      expected = np.linalg.svd(a)[1]
      np.testing.assert_array_almost_equal(res, expected)
  
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 2 for calling a global function with example usage: test_svd_execution(setup) without return types