code: def test_perspective_interpolation_type():
      spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
      epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
      tensor = torch.randint(0, 256, (3, 26, 26))
  
      res1 = F.perspective(
          tensor,
          startpoints=spoints,
          endpoints=epoints,
          interpolation=PIL.Image.BILINEAR,
      )
      res2 = F.perspective(
          tensor,
          startpoints=spoints,
          endpoints=epoints,
          interpolation=BILINEAR,
      )
      assert_equal(res1, res2)
  
imports: import colorsys
  import itertools
  import math
  import os
  from functools import partial
  from typing import Sequence
  import numpy
  import PIL.Image
  import pytest
  import torch
  import torchvision.transforms
  import torchvision.transforms._functional_pil
  import torchvision.transforms._functional_tensor
  import torchvision.transforms.functional
  from common_utils import _assert_approx_equal_tensor_to_pil, _assert_equal_tensor_to_pil, _create_data, _create_data_batch, _test_fn_on_batch, assert_equal, cpu_and_cuda, needs_cuda
  from torchvision.transforms import InterpolationMode
label_desc: Use image node_id 6 for calling a global function with example usage: test_perspective_interpolation_type() without return types