code: def test_lambda_transform():
      def negate_image(image, **kwargs):
          return -image
  
      def one_hot_mask(mask, num_channels, **kwargs):
          new_mask = np.eye(num_channels, dtype=np.uint8)[mask]
          return new_mask
  
      def vflip_bbox(bbox, **kwargs):
          return FGeometric.bbox_vflip(bbox, **kwargs)
  
      def vflip_keypoint(keypoint, **kwargs):
          return FGeometric.keypoint_vflip(keypoint, **kwargs)
  
      aug = A.Lambda(
          image=negate_image,
          mask=partial(one_hot_mask, num_channels=16),
          bbox=vflip_bbox,
          keypoint=vflip_keypoint,
          p=1,
      )
  
      output = aug(
          image=np.ones((10, 10, 3), dtype=np.float32),
          mask=np.tile(np.arange(0, 10), (10, 1)),
          bboxes=[(10, 15, 25, 35)],
          keypoints=[(20, 30, 40, 50)],
      )
      assert (output["image"] < 0).all()
      assert output["mask"].shape[2] == 16  # num_channels
      assert output["bboxes"] == [
          FGeometric.bbox_vflip((10, 15, 25, 35), 10, 10)
      ]
      assert output["keypoints"] == [
          FGeometric.keypoint_vflip((20, 30, 40, 50), 10, 10)
      ]
  
imports: import random
  from functools import partial
  import cv2
  import numpy
  import pytest
  import albumentations
  import albumentations.augmentations.functional
  import albumentations.augmentations.geometric.functional
  from albumentations.augmentations.blur.functional import gaussian_blur
  from .utils import get_dual_transforms, get_image_only_transforms, get_transforms
label_desc: Use image node_id 17 for calling a global function with example usage: test_lambda_transform() and returns: unknown, new_mask, FGeometric, FGeometric