code: def test_classify_in_context(
      self, model_file_type, max_results, expected_classification_result
  ):
      if model_file_type is ModelFileType.FILE_NAME:
          base_options = _BaseOptions(model_asset_path=self.model_path)
      elif model_file_type is ModelFileType.FILE_CONTENT:
          with open(self.model_path, "rb") as f:
              model_content = f.read()
          base_options = _BaseOptions(model_asset_buffer=model_content)
      else:
          # Should never happen
          raise ValueError("model_file_type is invalid.")
  
      options = _ImageClassifierOptions(
          base_options=base_options, max_results=max_results
      )
      with _ImageClassifier.create_from_options(options) as classifier:
          # Performs image classification on the input.
          image_result = classifier.classify(self.test_image)
          # Comparing results.
          test_utils.assert_proto_equals(
              self,
              image_result.to_pb2(),
              expected_classification_result.to_pb2(),
          )
  
imports: import enum
  import os
  from unittest import mock
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from mediapipe.python._framework_bindings import image
  from mediapipe.tasks.python.components.containers import category
  from mediapipe.tasks.python.components.containers import classification_result
  from mediapipe.tasks.python.components.containers import rect
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.test import test_utils
  from mediapipe.tasks.python.vision import image_classifier
  from mediapipe.tasks.python.vision.core import image_processing_options
  from mediapipe.tasks.python.vision.core import vision_task_running_mode
label_desc: Use image node_id 7 for calling the ImageClassifierTest obj's underlying member method code with example usage: obj.test_classify_in_context(model_file_type, max_results, expected_classification_result) without return types