code: def test_resize(self):
      g = self.create_empty_graph()
      input = g.addInput()
      input.setType(
          input.type()
          .with_dtype(torch.float)
          .with_sizes([4, 32, 64, 64])
      )
      none = g_op(g, "prim::Constant").setType(torch.NoneType.get())
      scales = self.insert_tensor_constant(
          g, torch.tensor([1, 1, 2, 2], dtype=torch.float)
      )
      resize = g_op(
          g,
          "Resize",
          input,
          none,
          scales,
          coordinate_transformation_mode_s="align_corners",
          cubic_coeff_a_f=-0.75,
          mode_s="linear",
          nearest_mode_s="floor",
      )
      self.run_test(
          g,
          resize.node(),
          expect_tensor("Float", shape=(4, 32, 128, 128)),
      )
  
imports: import io
  import numpy
  import onnx
  import pytorch_test_common
  import torch
  from pytorch_test_common import skipIfUnsupportedMinOpsetVersion
  from torch.onnx import _constants, utils
  from torch.onnx._globals import GLOBALS
  from torch.onnx._internal import jit_utils
  from torch.testing._internal import common_utils
label_desc: Use image node_id 21 for calling the TestONNXShapeInference obj's underlying member method code with example usage: obj.test_resize() without return types