code: def test_normalize_in_make_grid():
      t = torch.rand(5, 3, 10, 10) * 255
      norm_max = torch.tensor(1.0)
      norm_min = torch.tensor(0.0)
  
      grid = utils.make_grid(t, normalize=True)
      grid_max = torch.max(grid)
      grid_min = torch.min(grid)
  
      # Rounding the result to one decimal for comparison
      n_digits = 1
      rounded_grid_max = torch.round(grid_max * 10**n_digits) / (
          10**n_digits
      )
      rounded_grid_min = torch.round(grid_min * 10**n_digits) / (
          10**n_digits
      )
  
      assert_equal(
          norm_max,
          rounded_grid_max,
          msg="Normalized max is not equal to 1",
      )
      assert_equal(
          norm_min,
          rounded_grid_min,
          msg="Normalized min is not equal to 0",
      )
  
imports: import os
  import re
  import sys
  import tempfile
  from io import BytesIO
  import numpy
  import pytest
  import torch
  import torchvision.transforms.functional
  import torchvision.utils
  from common_utils import assert_equal, cpu_and_cuda
  from PIL import __version__, Image, ImageColor
  from torchvision.transforms.v2.functional import to_dtype
label_desc: Use image node_id 2 for calling a global function with example usage: test_normalize_in_make_grid() without return types