code: def test_pad(m, mul, device):
      fill = 127 if m == "constant" else 0
  
      # Test functional.pad (PIL and Tensor) with padding as single int
      _test_functional_op(
          F.pad,
          fn_kwargs={
              "padding": mul * 2,
              "fill": fill,
              "padding_mode": m,
          },
          device=device,
      )
      # Test functional.pad and transforms.Pad with padding as [int, ]
      fn_kwargs = meth_kwargs = {
          "padding": [mul * 2],
          "fill": fill,
          "padding_mode": m,
      }
      _test_op(
          F.pad,
          T.Pad,
          device=device,
          fn_kwargs=fn_kwargs,
          meth_kwargs=meth_kwargs,
      )
      # Test functional.pad and transforms.Pad with padding as list
      fn_kwargs = meth_kwargs = {
          "padding": [mul * 4, 4],
          "fill": fill,
          "padding_mode": m,
      }
      _test_op(
          F.pad,
          T.Pad,
          device=device,
          fn_kwargs=fn_kwargs,
          meth_kwargs=meth_kwargs,
      )
      # Test functional.pad and transforms.Pad with padding as tuple
      fn_kwargs = meth_kwargs = {
          "padding": (mul * 2, 2, 2, mul * 2),
          "fill": fill,
          "padding_mode": m,
      }
      _test_op(
          F.pad,
          T.Pad,
          device=device,
          fn_kwargs=fn_kwargs,
          meth_kwargs=meth_kwargs,
      )
  
imports: import os
  import sys
  import numpy
  import PIL.Image
  import pytest
  import torch
  from common_utils import _assert_approx_equal_tensor_to_pil, _assert_equal_tensor_to_pil, _create_data, _create_data_batch, assert_equal, cpu_and_cuda, float_dtypes, get_tmp_dir, int_dtypes
  from torchvision import transforms
  from torchvision.transforms import functional, InterpolationMode
  from torchvision.transforms.autoaugment import _apply_op
label_desc: Use image node_id 8 for calling a global function with example usage: test_pad(m, mul, device) without return types