code: def test_mixup_transform_multi_label_image_batch(self):
      mixup_alpha = 2.0
      mixup_transform = MixupTransform(mixup_alpha, None)
      sample = {
          "input": torch.rand(4, 3, 224, 224, dtype=torch.float32),
          "target": torch.as_tensor(
              [[1, 0, 0, 0], [0, 1, 0, 1], [0, 0, 1, 1], [0, 1, 1, 1]],
              dtype=torch.int32,
          ),
      }
      sample_mixup = mixup_transform(sample)
      self.assertTrue(
          sample["input"].shape == sample_mixup["input"].shape
      )
      self.assertTrue(
          sample["target"].shape == sample_mixup["target"].shape
      )
  
file_path: ClassyVision/test/dataset_transforms_mixup_test.py
imports: import unittest
  import torch
  from classy_vision.dataset.transforms.mixup import MixupTransform
label_desc: Use image node_id 6 for calling the DatasetTransformsMixupTest obj's underlying member method code with example usage: obj.test_mixup_transform_multi_label_image_batch() without return types