code: def test_augmix(device, fill):
      tensor = torch.randint(
          0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device
      )
      batch_tensors = torch.randint(
          0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device
      )
  
      class DeterministicAugMix(T.AugMix):
          def _sample_dirichlet(
              self, params: torch.Tensor
          ) -> torch.Tensor:
              # patch the method to ensure that the order of rand calls doesn't affect the outcome
              return params.softmax(dim=-1)
  
      transform = DeterministicAugMix(fill=fill)
      s_transform = torch.jit.script(transform)
      for _ in range(25):
          _test_transform_vs_scripted(transform, s_transform, tensor)
          _test_transform_vs_scripted_on_batch(
              transform, s_transform, batch_tensors
          )
  
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 33 for calling a global function with example usage: test_augmix(device, fill) and returns: params