code: def test_tile_execution(setup):
      a_data = np.array([0, 1, 2])
      a = tensor(a_data, chunk_size=2)
  
      t = tile(a, 2)
  
      res = t.execute().fetch()
      expected = np.tile(a_data, 2)
      np.testing.assert_equal(res, expected)
  
      t = tile(a, (2, 2))
  
      res = t.execute().fetch()
      expected = np.tile(a_data, (2, 2))
      np.testing.assert_equal(res, expected)
  
      t = tile(a, (2, 1, 2))
  
      res = t.execute().fetch()
      expected = np.tile(a_data, (2, 1, 2))
      np.testing.assert_equal(res, expected)
  
      b_data = np.array([[1, 2], [3, 4]])
      b = tensor(b_data, chunk_size=1)
  
      t = tile(b, 2)
  
      res = t.execute().fetch()
      expected = np.tile(b_data, 2)
      np.testing.assert_equal(res, expected)
  
      t = tile(b, (2, 1))
  
      res = t.execute().fetch()
      expected = np.tile(b_data, (2, 1))
      np.testing.assert_equal(res, expected)
  
      c_data = np.array([1, 2, 3, 4])
      c = tensor(c_data, chunk_size=3)
  
      t = tile(c, (4, 1))
  
      res = t.execute().fetch()
      expected = np.tile(c_data, (4, 1))
      np.testing.assert_equal(res, expected)
  
imports: import numpy
  import scipy.sparse
  import pandas
  import pytest
  from ....None import dataframe
  from ....None import tensor
  from ....None import execute, fetch
  from ....tests.core import require_cupy
  from ...datasource import tensor, ones, zeros, arange
  from ..None import copyto, transpose, moveaxis, broadcast_to, broadcast_arrays, where, expand_dims, rollaxis, atleast_1d, atleast_2d, atleast_3d, argwhere, array_split, split, hsplit, vsplit, dsplit, roll, squeeze, diff, ediff1d, flip, flipud, fliplr, repeat, tile, isin, searchsorted, unique, sort, argsort, partition, argpartition, topk, argtopk, trapz, shape, to_gpu, to_cpu, swapaxes
label_desc: Use image node_id 25 for calling a global function with example usage: test_tile_execution(setup) without return types