code: def test_call_numpy(self):
      # Initialize image_processing
      image_processing = self.image_processing_class(
          **self.image_processor_dict
      )
      # create random numpy tensors
      image_inputs = self.image_processor_tester.prepare_image_inputs(
          equal_resolution=False, numpify=True
      )
      for image in image_inputs:
          self.assertIsInstance(image, np.ndarray)
  
      # Test not batched input
      encoded_images = image_processing(
          image_inputs[0], return_tensors="pt"
      ).pixel_values
      expected_output_image_shape = (
          self.image_processor_tester.expected_output_image_shape(
              [image_inputs[0]]
          )
      )
      self.assertEqual(
          tuple(encoded_images.shape), (1, *expected_output_image_shape)
      )
  
      # Test batched
      encoded_images = image_processing(
          image_inputs, return_tensors="pt"
      ).pixel_values
      expected_output_image_shape = (
          self.image_processor_tester.expected_output_image_shape(
              image_inputs
          )
      )
      self.assertEqual(
          tuple(encoded_images.shape),
          (
              self.image_processor_tester.batch_size,
              *expected_output_image_shape,
          ),
      )
  
imports: import json
  import os
  import pathlib
  import tempfile
  from transformers import BatchFeature
  from transformers.image_utils import AnnotationFormat, AnnotionFormat
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
label_desc: Use image node_id 7 for calling the ImageProcessingTestMixin obj's underlying member method code with example usage: obj.test_call_numpy() without return types