code: def get_deep_features(
      images, model_name, batch_size=64, verbose=True
  ):
      """
      Extracts features from images from a specific model.
  
      Parameters
      ----------
      images : SArray
          Input data.
  
      model_name : string
          string optional
          Uses a pretrained model to bootstrap an image classifier:
  
             - "resnet-50" : Uses a pretrained resnet model.
                             Exported Core ML model will be ~90M.
  
             - "squeezenet_v1.1" : Uses a pretrained squeezenet model.
                                   Exported Core ML model will be ~4.7M.
  
             - "VisionFeaturePrint_Scene": Uses an OS internal feature extractor.
                                            Only on available on iOS 12.0+,
                                            macOS 10.14+ and tvOS 12.0+.
                                            Exported Core ML model will be ~41K.
  
          Models are downloaded from the internet if not available locally. Once
          downloaded, the models are cached for future use.
  
      Returns
      -------
      out : SArray
          Returns an SArray with all the extracted features.
  
      See Also
      --------
      turicreate.image_classifier.create
      turicreate.image_similarity.create
  
      Examples
      --------
      >>> url = 'https://static.turi.com/datasets/images/nested'
      >>> image_sframe = turicreate.load_images(url)
      >>> image_sarray = image_sframe["image"]
      >>> deep_features_sframe = turicreate.image_analysis.get_deep_features(image_sarray, model_name="resnet-50")
      """
  
      # Check model parameter
      allowed_models = list(_pre_trained_models.IMAGE_MODELS.keys())
      if _mac_ver() >= (10, 14):
          allowed_models.append("VisionFeaturePrint_Scene")
      _tkutl._check_categorical_option_type(
          "model", model_name, allowed_models
      )
  
      # Check images parameter
      if not isinstance(images, _tc.SArray):
          raise TypeError(
              "Unrecognized type for 'images'. An SArray is expected."
          )
      if len(images) == 0:
          raise _ToolkitError(
              "Unable to extract features on an empty SArray object"
          )
  
      if batch_size < 1:
          raise ValueError(
              "'batch_size' must be greater than or equal to 1"
          )
  
      # Extract features
      feature_extractor = (
          _image_feature_extractor._create_feature_extractor(model_name)
      )
      images_sf = _tc.SFrame({"image": images})
      return feature_extractor.extract_features(
          images_sf, "image", verbose=verbose, batch_size=batch_size
      )
  
file_path: turicreate/src/python/turicreate/toolkits/image_analysis/image_analysis.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits._main import ToolkitError
  import turicreate.toolkits._internal_utils
  from .._internal_utils import _mac_ver
  from ..None import _pre_trained_models
  from ...data_structures.image import Image
  from ..None import _image_feature_extractor
label_desc: Use image node_id 4 for calling a global function with example usage: get_deep_features(images, model_name, batch_size, verbose) and returns: feature_extractor