code: def test_embed_in_context(
      self, l2_normalize, quantize, model_file_type, expected_similarity
  ):
      # Creates embedder.
      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 = _ImageEmbedderOptions(
          base_options=base_options,
          l2_normalize=l2_normalize,
          quantize=quantize,
      )
  
      with _ImageEmbedder.create_from_options(options) as embedder:
          # Extracts both embeddings.
          image_result = embedder.embed(self.test_image)
          crop_result = embedder.embed(self.test_cropped_image)
  
          # Checks cosine similarity.
          self._check_cosine_similarity(
              image_result, crop_result, expected_similarity
          )
  
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 embedding_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_embedder
  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 10 for calling the ImageEmbedderTest obj's underlying member method code with example usage: obj.test_embed_in_context(l2_normalize, quantize, model_file_type, expected_similarity) without return types