code: def test_perspective_batch(device, dims_and_points, dt):
      if dt == torch.float16 and device == "cpu":
          # skip float16 on CPU case
          return
  
      data_dims, (spoints, epoints) = dims_and_points
  
      batch_tensors = _create_data_batch(
          *data_dims, num_samples=4, device=device
      )
      if dt is not None:
          batch_tensors = batch_tensors.to(dtype=dt)
  
      # Ignore the equivalence between scripted and regular function on float16 cuda. The pixels at
      # the border may be entirely different due to small rounding errors.
      scripted_fn_atol = (
          -1 if (dt == torch.float16 and device == "cuda") else 1e-8
      )
      _test_fn_on_batch(
          batch_tensors,
          F.perspective,
          scripted_fn_atol=scripted_fn_atol,
          startpoints=spoints,
          endpoints=epoints,
          interpolation=NEAREST,
      )
  
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 5 for calling a global function with example usage: test_perspective_batch(device, dims_and_points, dt) without return types