class_docstr: The abstract class for Turi Create recommender system models. This class
  defines methods common to all recommender system models, but leaves unique
  model details to separate model classes.
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docstr: Return a score prediction for the user ids and item ids in the provided
  data set.
  
  Parameters
  ----------
  dataset : SFrame
      Dataset in the same form used for training.
  
  new_observation_data : SFrame, optional
      ``new_observation_data`` gives additional observation data
      to the model, which may be used by the models to improve
      score accuracy.  Must be in the same format as the
      observation data passed to ``create``.  How this data is
      used varies by model.
  
  new_user_data : SFrame, optional
      ``new_user_data`` may give additional user data to the
      model.  If present, scoring is done with reference to this
      new information.  If there is any overlap with the side
      information present at training time, then this new side
      data is preferred.  Must be in the same format as the user
      data passed to ``create``.
  
  new_item_data : SFrame, optional
      ``new_item_data`` may give additional item data to the
      model.  If present, scoring is done with reference to this
      new information.  If there is any overlap with the side
      information present at training time, then this new side
      data is preferred.  Must be in the same format as the item
      data passed to ``create``.
  
  Returns
  -------
  out : SArray
      An SArray with predicted scores for each given observation
      predicted by the model.
  
  See Also
  --------
  recommend, evaluate
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code: def predict(
      self,
      dataset,
      new_observation_data=None,
      new_user_data=None,
      new_item_data=None,
  ):
      """
      Return a score prediction for the user ids and item ids in the provided
      data set.
  
      Parameters
      ----------
      dataset : SFrame
          Dataset in the same form used for training.
  
      new_observation_data : SFrame, optional
          ``new_observation_data`` gives additional observation data
          to the model, which may be used by the models to improve
          score accuracy.  Must be in the same format as the
          observation data passed to ``create``.  How this data is
          used varies by model.
  
      new_user_data : SFrame, optional
          ``new_user_data`` may give additional user data to the
          model.  If present, scoring is done with reference to this
          new information.  If there is any overlap with the side
          information present at training time, then this new side
          data is preferred.  Must be in the same format as the user
          data passed to ``create``.
  
      new_item_data : SFrame, optional
          ``new_item_data`` may give additional item data to the
          model.  If present, scoring is done with reference to this
          new information.  If there is any overlap with the side
          information present at training time, then this new side
          data is preferred.  Must be in the same format as the item
          data passed to ``create``.
  
      Returns
      -------
      out : SArray
          An SArray with predicted scores for each given observation
          predicted by the model.
  
      See Also
      --------
      recommend, evaluate
      """
  
      if new_observation_data is None:
          new_observation_data = _SFrame()
      if new_user_data is None:
          new_user_data = _SFrame()
      if new_item_data is None:
          new_item_data = _SFrame()
  
      dataset = self.__prepare_dataset_parameter(dataset)
  
      def check_type(arg, arg_name, required_type, allowed_types):
          if not isinstance(arg, required_type):
              raise TypeError(
                  "Parameter "
                  + arg_name
                  + " must be of type(s) "
                  + (", ".join(allowed_types))
                  + "; Type '"
                  + str(type(arg))
                  + "' not recognized."
              )
  
      check_type(
          new_observation_data,
          "new_observation_data",
          _SFrame,
          ["SFrame"],
      )
      check_type(new_user_data, "new_user_data", _SFrame, ["SFrame"])
      check_type(new_item_data, "new_item_data", _SFrame, ["SFrame"])
  
      response = self.__proxy__.predict(
          dataset, new_user_data, new_item_data
      )
      return response["prediction"]
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  import random
  import array
  import logging
  import time
  import six
  from turicreate.toolkits import _coreml_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits._model import Model
  from turicreate.toolkits._internal_utils import _toolkit_repr_print, _precomputed_field
  import turicreate.aggregate
  from turicreate.data_structures.sarray import SArray
  from turicreate.data_structures.sframe import SFrame
  from turicreate._deps import numpy, HAS_NUMPY
label_desc: Use image node_id 13 for calling the _Recommender obj's underlying member method code with example usage: obj.predict(dataset, new_observation_data, new_user_data, new_item_data) and returns: response