code: def _create_data(height=3, width=3, channels=3, device="cpu"):
      # TODO: When all relevant tests are ported to pytest, turn this into a module-level fixture
      tensor = torch.randint(
          0,
          256,
          (channels, height, width),
          dtype=torch.uint8,
          device=device,
      )
      data = tensor.permute(1, 2, 0).contiguous().cpu().numpy()
      mode = "RGB"
      if channels == 1:
          mode = "L"
          data = data[..., 0]
      pil_img = Image.fromarray(data, mode=mode)
      return tensor, pil_img
  
imports: import contextlib
  import functools
  import itertools
  import os
  import pathlib
  import random
  import re
  import shutil
  import sys
  import tempfile
  import warnings
  from subprocess import CalledProcessError, check_output, STDOUT
  import numpy
  import PIL.Image
  import pytest
  import torch
  import torch.testing
  from PIL import Image
  from torch.testing._comparison import BooleanPair, NonePair, not_close_error_metas, NumberPair, TensorLikePair
  from torchvision import io, tv_tensors
  from torchvision.transforms._functional_tensor import _max_value
  from torchvision.transforms.v2.functional import to_image, to_pil_image
label_desc: Use image node_id 14 for calling a global function with example usage: _create_data(height, width, channels, device) and returns: tensor,  pil_img