code: def test_resize_upsample_sizes_cubic(device):
      data = data_1x1x4x4
      sizes = np.array([1.0, 1.0, 9.0, 10.0], dtype=np.float32)
  
      # from onnx.backend.test.case.node.resize import interpolate_nd, cubic_coeffs
      # expected = interpolate_nd(data, lambda x, _: cubic_coeffs(x, A=-0.5),
      #                           output_size=sizes.astype(np.int64))
      expected = np.array(
          [
              [
                  [
                      [
                          0.63671948,
                          0.76971948,
                          1.14771948,
                          1.60571948,
                          2.01021948,
                          2.41021948,
                          2.81471948,
                          3.27271948,
                          3.65071948,
                          3.78371948,
                      ],
                      [
                          1.36168519,
                          1.49468519,
                          1.87268519,
                          2.33068519,
                          2.73518519,
                          3.13518519,
                          3.53968519,
                          3.99768519,
                          4.37568519,
                          4.50868519,
                      ],
                      [
                          3.18610219,
                          3.31910219,
                          3.69710219,
                          4.15510219,
                          4.55960219,
                          4.95960219,
                          5.36410219,
                          5.82210219,
                          6.20010219,
                          6.33310219,
                      ],
                      [
                          5.14872222,
                          5.28172222,
                          5.65972222,
                          6.11772222,
                          6.52222222,
                          6.92222222,
                          7.32672222,
                          7.78472222,
                          8.16272222,
                          8.29572222,
                      ],
                      [
                          6.9265,
                          7.0595,
                          7.4375,
                          7.8955,
                          8.3,
                          8.7,
                          9.1045,
                          9.5625,
                          9.9405,
                          10.0735,
                      ],
                      [
                          8.70427778,
                          8.83727778,
                          9.21527778,
                          9.67327778,
                          10.07777778,
                          10.47777778,
                          10.88227778,
                          11.34027778,
                          11.71827778,
                          11.85127778,
                      ],
                      [
                          10.66689781,
                          10.79989781,
                          11.17789781,
                          11.63589781,
                          12.04039781,
                          12.44039781,
                          12.84489781,
                          13.30289781,
                          13.68089781,
                          13.81389781,
                      ],
                      [
                          12.49131481,
                          12.62431481,
                          13.00231481,
                          13.46031481,
                          13.86481481,
                          14.26481481,
                          14.66931481,
                          15.12731481,
                          15.50531481,
                          15.63831481,
                      ],
                      [
                          13.21628052,
                          13.34928052,
                          13.72728052,
                          14.18528052,
                          14.58978052,
                          14.98978052,
                          15.39428052,
                          15.85228052,
                          16.23028052,
                          16.36328052,
                      ],
                  ]
              ]
          ],
          dtype=np.float32,
      )
  
      def resize_fn(input_data):
          return fn.experimental.tensor_resize(
              input_data,
              sizes=sizes,
              alignment=0,
              interp_type=types.INTERP_CUBIC,
              antialias=False,
          )
  
      run_and_compare(expected, data, device, resize_fn)
  
file_path: DALI/dali/test/python/operator_2/test_tensor_resize.py
imports: import nvidia.dali.types
  from nvidia.dali import pipeline_def, fn
  from nose2.tools import params
  import numpy
  from test_utils import as_array
label_desc: Use image node_id 10 for calling a global function with example usage: test_resize_upsample_sizes_cubic(device) and returns: fn