code: def test_arrow_variable_shaped_tensor_array_slice():
      shapes = [(2, 2), (3, 3), (4, 4)]
      cumsum_sizes = np.cumsum(
          [0] + [np.prod(shape) for shape in shapes[:-1]]
      )
      arrs = [
          np.arange(offset, offset + np.prod(shape)).reshape(shape)
          for offset, shape in zip(cumsum_sizes, shapes)
      ]
      arr = np.array(arrs, dtype=object)
      ata = ArrowVariableShapedTensorArray.from_numpy(arr)
      assert isinstance(ata.type, ArrowVariableShapedTensorType)
      assert len(ata) == len(arr)
      indices = [0, 1, 2]
      for i in indices:
          np.testing.assert_array_equal(ata[i], arr[i])
      slices = [
          slice(0, 1),
          slice(1, 2),
          slice(2, 3),
          slice(0, 2),
          slice(1, 3),
          slice(0, 3),
      ]
      for slice_ in slices:
          ata_slice = ata[slice_]
          ata_slice_np = ata_slice.to_numpy()
          arr_slice = arr[slice_]
          # Check for equivalent dtypes and shapes.
          assert ata_slice_np.dtype == arr_slice.dtype
          assert ata_slice_np.shape == arr_slice.shape
          # Iteration over tensor array slices triggers NumPy conversion.
          for o, e in zip(ata_slice, arr_slice):
              np.testing.assert_array_equal(o, e)
  
imports: import itertools
  import numpy
  import pandas
  from packaging.version import parse
  import pyarrow
  import pytest
  from ray.air.util.tensor_extensions.arrow import ArrowTensorArray, ArrowTensorType, ArrowVariableShapedTensorArray, ArrowVariableShapedTensorType
  from ray.air.util.tensor_extensions.pandas import TensorArray, TensorDtype
  from ray.air.util.tensor_extensions.utils import create_ragged_ndarray
  from ray._private.utils import _get_pyarrow_version
label_desc: Use image node_id 10 for calling a global function with example usage: test_arrow_variable_shaped_tensor_array_slice() without return types