code: def _create_data_batch(
      height=3, width=3, channels=3, num_samples=4, device="cpu"
  ):
      # TODO: When all relevant tests are ported to pytest, turn this into a module-level fixture
      batch_tensor = torch.randint(
          0,
          256,
          (num_samples, channels, height, width),
          dtype=torch.uint8,
          device=device,
      )
      return batch_tensor
  
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 15 for calling a global function with example usage: _create_data_batch(height, width, channels, num_samples, device) and returns: batch_tensor