code: def test_triu_execution(setup):
      a = arange(24, chunk_size=2).reshape(2, 3, 4)
  
      t = triu(a)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(24).reshape(2, 3, 4))
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=1)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(24).reshape(2, 3, 4), k=1)
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=2)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(24).reshape(2, 3, 4), k=2)
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=-1)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(24).reshape(2, 3, 4), k=-1)
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=-2)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(24).reshape(2, 3, 4), k=-2)
      np.testing.assert_equal(res, expected)
  
      # test sparse
      a = arange(12, chunk_size=2).reshape(3, 4).tosparse()
  
      t = triu(a)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(12).reshape(3, 4))
      assert isinstance(res, SparseNDArray)
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=1)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(12).reshape(3, 4), k=1)
      assert isinstance(res, SparseNDArray)
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=2)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(12).reshape(3, 4), k=2)
      assert isinstance(res, SparseNDArray)
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=-1)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(12).reshape(3, 4), k=-1)
      assert isinstance(res, SparseNDArray)
      np.testing.assert_equal(res, expected)
  
      t = triu(a, k=-2)
  
      res = t.execute().fetch()
      expected = np.triu(np.arange(12).reshape(3, 4), k=-2)
      assert isinstance(res, SparseNDArray)
      np.testing.assert_equal(res, expected)
  
      raw = np.asfortranarray(np.random.rand(10, 7))
      a = tensor(raw, chunk_size=3)
  
      t = triu(a, k=-2)
  
      res = t.execute().fetch()
      expected = np.triu(raw, k=-2)
      np.testing.assert_array_equal(res, expected)
      assert res.flags["C_CONTIGUOUS"] == expected.flags["C_CONTIGUOUS"]
      assert res.flags["F_CONTIGUOUS"] == expected.flags["F_CONTIGUOUS"]
  
imports: import tempfile
  import shutil
  import os
  import time
  import numpy
  import pandas
  import pytest
  from ....None import tensor
  from ....None import dataframe
  from ....lib.sparse import SparseNDArray
  from ....tests.core import require_cupy
  from ....utils import lazy_import
  from ...lib import nd_grid
  from ..None import tensor, ones_like, zeros, zeros_like, full, full_like, arange, empty, empty_like, diag, diagflat, eye, linspace, meshgrid, indices, triu, tril, from_dataframe, fromtiledb, fromhdf5, fromzarr
label_desc: Use image node_id 13 for calling a global function with example usage: test_triu_execution(setup) without return types