code: def test_qr_execution(setup):
      rs = np.random.RandomState(0)
      data = rs.randn(18, 6)
  
      a = tensor(data, chunk_size=(3, 6))
      q, r = qr(a)
      t = q.dot(r)
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      a = tensor(data, chunk_size=(9, 6))
      q, r = qr(a)
      t = q.dot(r)
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      a = tensor(data, chunk_size=3)
      q, r = qr(a)
      t = q.dot(r)
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      # test for Short-and-Fat QR
      data = rs.randn(6, 18)
  
      a = tensor(data, chunk_size=(6, 9))
      q, r = qr(a, method="sfqr")
      t = q.dot(r)
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      a = tensor(data, chunk_size=(3, 3))
      q, r = qr(a, method="sfqr")
      t = q.dot(r)
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, data)
  
      a = tensor(data, chunk_size=(6, 3))
      q, r = qr(a, method="sfqr")
      t = q.dot(r)
  
      res = t.execute().fetch()
      np.testing.assert_array_almost_equal(res, 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 1 for calling a global function with example usage: test_qr_execution(setup) without return types