code: def test_arrow_variable_shaped_tensor_array_roundtrip_contiguous_optimization():
      # Test that a roundtrip on slices of an already-contiguous 1D base array does not
      # create any unnecessary copies.
      base = np.arange(6)
      base_address = base.__array_interface__["data"][0]
      arr = np.array([base[:2], base[2:]], dtype=object)
      ata = ArrowVariableShapedTensorArray.from_numpy(arr)
      assert isinstance(ata.type, ArrowVariableShapedTensorType)
      assert len(ata) == len(arr)
      assert (
          ata.storage.field("data").buffers()[3].address == base_address
      )
      out = ata.to_numpy()
      for o, a in zip(out, arr):
          assert o.base.address == base_address
          np.testing.assert_array_equal(o, a)
  
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 9 for calling a global function with example usage: test_arrow_variable_shaped_tensor_array_roundtrip_contiguous_optimization() without return types