code: def test_mixup_transform_single_label_image_batch(self):
      mixup_alpha = 2.0
      num_classes = 3
  
      for mode in ["batch", "pair", "elem"]:
          mixup_transform = MixupTransform(
              mixup_alpha, num_classes, mode=mode
          )
          sample = {
              "input": torch.rand(4, 3, 224, 224, dtype=torch.float32),
              "target": torch.as_tensor(
                  [0, 1, 2, 2], dtype=torch.int32
              ),
          }
          sample_mixup = mixup_transform(sample)
          self.assertTrue(
              sample["input"].shape == sample_mixup["input"].shape
          )
          self.assertTrue(sample_mixup["target"].shape[0] == 4)
          self.assertTrue(sample_mixup["target"].shape[1] == 3)
  
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 1 for calling the DatasetTransformsMixupTest obj's underlying member method code with example usage: obj.test_mixup_transform_single_label_image_batch() without return types