code: def test_data_source():
      from ...base.broadcast_to import TensorBroadcastTo
  
      data = np.random.random((10, 3))
      t = tensor(data, chunk_size=2)
      assert t.op.gpu is None
      t = tile(t)
      assert (t.chunks[0].op.data == data[:2, :2]).all()
      assert (t.chunks[1].op.data == data[:2, 2:3]).all()
      assert (t.chunks[2].op.data == data[2:4, :2]).all()
      assert (t.chunks[3].op.data == data[2:4, 2:3]).all()
  
      assert t.key == tile(tensor(data, chunk_size=2)).key
      assert t.key != tile(tensor(data, chunk_size=3)).key
      assert (
          t.key
          != tile(tensor(np.random.random((10, 3)), chunk_size=2)).key
      )
  
      t = tensor(data, chunk_size=2, gpu=True)
      t = tile(t)
  
      assert t.op.gpu is True
      assert t.chunks[0].op.gpu is True
  
      t = full((2, 2), 2, dtype="f4")
      assert t.op.gpu is None
      assert t.shape == (2, 2)
      assert t.dtype == np.float32
  
      t = full((2, 2), [1.0, 2.0], dtype="f4")
      assert t.shape == (2, 2)
      assert t.dtype == np.float32
      assert isinstance(t.op, TensorBroadcastTo)
  
      with pytest.raises(ValueError):
          full((2, 2), [1.0, 2.0, 3.0], dtype="f4")
  
imports: import shutil
  import tempfile
  from copy import copy
  import numpy
  import scipy.sparse
  import pytest
  from ....None import dataframe
  from ....core import enter_mode, tile
  from ...None import ones, zeros, tensor, full, arange, diag, linspace, triu, tril, ones_like
  from ...core import Tensor, SparseTensor
  from ..None import array, fromtiledb, TensorTileDBDataSource, fromdense, asarray, ascontiguousarray, asfortranarray
  from ..tri import TensorTriu, TensorTril
  from ..zeros import TensorZeros
  from ..from_dense import DenseToSparse
  from ..array import CSRMatrixDataSource
  from ..ones import TensorOnes, TensorOnesLike
  from ..from_dataframe import from_dataframe
label_desc: Use image node_id 6 for calling a global function with example usage: test_data_source() without return types