code: def test_masking(self):
      # Initialize image_processing
      random.seed(1234)
      image_processing = self.image_processing_class(
          **self.image_processor_dict
      )
      image_inputs = self.image_processor_tester.prepare_image_inputs(
          equal_resolution=False, torchify=True
      )
  
      # Test not batched input
      encoded_images = image_processing(
          image_inputs[0], return_image_mask=True, return_tensors="pt"
      )
      self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)
  
imports: import random
  import unittest
  import numpy
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 10 for calling the FlavaImageProcessingTest obj's underlying member method code with example usage: obj.test_masking() without return types