code: def create(
      data,
      target,
      backgrounds=None,
      batch_size=0,
      max_iterations=0,
      verbose=True,
  ):
      """
      Create a :class:`OneShotObjectDetector` model. Note: The One Shot Object Detector
      is currently in beta.
  
      Parameters
      ----------
      data : SFrame | tc.Image
          A single starter image or an SFrame that contains the starter images
          along with their corresponding labels.  These image(s) can be in either
          RGB or RGBA format. They should not be padded.
  
      target : string
          Name of the target (when data is a single image) or the target column
          name (when data is an SFrame of images).
  
      backgrounds : optional SArray
          A list of backgrounds used for synthetic data generation. When set to
          None, a set of default backgrounds are downloaded and used.
  
      batch_size : int
          The number of images per training iteration. If 0, then it will be
          automatically determined based on resource availability.
  
      max_iterations : int
          The number of training iterations. If 0, then it will be automatically
          be determined based on the amount of data you provide.
  
      verbose : bool optional
          If True, print progress updates and model details.
  
      Examples
      --------
      .. sourcecode:: python
  
          # Train an object detector model
          >>> model = turicreate.one_shot_object_detector.create(train_data, label = 'cards')
  
          # Make predictions on the training set and as column to the SFrame
          >>> test_data['predictions'] = model.predict(test_data)
      """
      if not isinstance(data, _tc.SFrame) and not isinstance(
          data, _tc.Image
      ):
          raise TypeError("'data' must be of type SFrame or tc.Image.")
      if isinstance(data, _tc.SFrame) and len(data) == 0:
          raise _ToolkitError("'data' can not be an empty SFrame")
  
      augmented_data = _preview_synthetic_training_data(
          data, target, backgrounds
      )
  
      model = _tc.object_detector.create(
          augmented_data,
          batch_size=batch_size,
          max_iterations=max_iterations,
          verbose=verbose,
      )
  
      if isinstance(data, _tc.SFrame):
          num_starter_images = len(data)
      else:
          num_starter_images = 1
  
      state = {
          "detector": model,
          "target": target,
          "num_classes": model.num_classes,
          "num_starter_images": num_starter_images,
          "_detector_version": _ObjectDetector._CPP_OBJECT_DETECTOR_VERSION,
      }
      return OneShotObjectDetector(state)
  
file_path: turicreate/src/python/turicreate/toolkits/one_shot_object_detector/one_shot_object_detector.py
imports: import turicreate
  from turicreate.toolkits._model import CustomModel
  from turicreate.toolkits._model import PythonProxy, ToolkitError
  from turicreate.toolkits.object_detector.object_detector import ObjectDetector
  from turicreate.toolkits.one_shot_object_detector.util._augmentation import preview_synthetic_training_data
  import turicreate.toolkits._internal_utils
label_desc: Use image node_id 1 for calling a global function with example usage: create(data, target, backgrounds, batch_size, max_iterations, verbose) and returns: OneShotObjectDetector