class_docstr: A trained model using C++ implementation that is ready to use for classification
  or export to CoreML.
  
  This model should not be constructed directly.
class_docstr_tok: ["A","trained","model","using","C++","implementation","that","is","ready","to","use","for","classification","or","export","to","CoreML",".","This","model","should","not","be","constructed","directly","."]
docstr: Save the model in Core ML format. The Core ML model takes an image of
  fixed size as input and produces two output arrays: `confidence` and
  `coordinates`.
  
  The first one, `confidence` is an `N`-by-`C` array, where `N` is the
  number of instances predicted and `C` is the number of classes. The
  number `N` is fixed and will include many low-confidence predictions.
  The instances are not sorted by confidence, so the first one will
  generally not have the highest confidence (unlike in `predict`). Also
  unlike the `predict` function, the instances have not undergone
  what is called `non-maximum suppression`, which means there could be
  several instances close in location and size that have all discovered
  the same object instance. Confidences do not need to sum to 1 over the
  classes; any remaining probability is implied as confidence there is no
  object instance present at all at the given coordinates. The classes
  appear in the array alphabetically sorted.
  
  The second array `coordinates` is of size `N`-by-4, where the first
  dimension `N` again represents instances and corresponds to the
  `confidence` array. The second dimension represents `x`, `y`, `width`,
  `height`, in that order.  The values are represented in relative
  coordinates, so (0.5, 0.5) represents the center of the image and (1,
  1) the bottom right corner. You will need to multiply the relative
  values with the original image size before you resized it to the fixed
  input size to get pixel-value coordinates similar to `predict`.
  
  See Also
  --------
  save
  
  Parameters
  ----------
  filename : string
      The path of the file where we want to save the Core ML model.
  
  include_non_maximum_suppression : bool
      Non-maximum suppression is only available in iOS 12+.
      A boolean parameter to indicate whether the Core ML model should be
      saved with built-in non-maximum suppression or not.
      This parameter is set to True by default.
  
  iou_threshold : float
      Threshold value for non-maximum suppression. Non-maximum suppression
      prevents multiple bounding boxes appearing over a single object.
      This threshold, set between 0 and 1, controls how aggressive this
      suppression is. A value of 1 means no maximum suppression will
      occur, while a value of 0 will maximally suppress neighboring
      boxes around a prediction.
  
  confidence_threshold : float
      Only return predictions above this level of confidence. The
      threshold can range from 0 to 1.
  
  Examples
  --------
  >>> model.export_coreml('detector.mlmodel')
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code: def export_coreml(
      self,
      filename,
      include_non_maximum_suppression=True,
      iou_threshold=None,
      confidence_threshold=None,
  ):
      """
      Save the model in Core ML format. The Core ML model takes an image of
      fixed size as input and produces two output arrays: `confidence` and
      `coordinates`.
  
      The first one, `confidence` is an `N`-by-`C` array, where `N` is the
      number of instances predicted and `C` is the number of classes. The
      number `N` is fixed and will include many low-confidence predictions.
      The instances are not sorted by confidence, so the first one will
      generally not have the highest confidence (unlike in `predict`). Also
      unlike the `predict` function, the instances have not undergone
      what is called `non-maximum suppression`, which means there could be
      several instances close in location and size that have all discovered
      the same object instance. Confidences do not need to sum to 1 over the
      classes; any remaining probability is implied as confidence there is no
      object instance present at all at the given coordinates. The classes
      appear in the array alphabetically sorted.
  
      The second array `coordinates` is of size `N`-by-4, where the first
      dimension `N` again represents instances and corresponds to the
      `confidence` array. The second dimension represents `x`, `y`, `width`,
      `height`, in that order.  The values are represented in relative
      coordinates, so (0.5, 0.5) represents the center of the image and (1,
      1) the bottom right corner. You will need to multiply the relative
      values with the original image size before you resized it to the fixed
      input size to get pixel-value coordinates similar to `predict`.
  
      See Also
      --------
      save
  
      Parameters
      ----------
      filename : string
          The path of the file where we want to save the Core ML model.
  
      include_non_maximum_suppression : bool
          Non-maximum suppression is only available in iOS 12+.
          A boolean parameter to indicate whether the Core ML model should be
          saved with built-in non-maximum suppression or not.
          This parameter is set to True by default.
  
      iou_threshold : float
          Threshold value for non-maximum suppression. Non-maximum suppression
          prevents multiple bounding boxes appearing over a single object.
          This threshold, set between 0 and 1, controls how aggressive this
          suppression is. A value of 1 means no maximum suppression will
          occur, while a value of 0 will maximally suppress neighboring
          boxes around a prediction.
  
      confidence_threshold : float
          Only return predictions above this level of confidence. The
          threshold can range from 0 to 1.
  
      Examples
      --------
      >>> model.export_coreml('detector.mlmodel')
      """
      options = {}
      options[
          "include_non_maximum_suppression"
      ] = include_non_maximum_suppression
      options["version"] = self._get_version()
      if confidence_threshold is not None:
          options["confidence_threshold"] = confidence_threshold
      if iou_threshold is not None:
          options["iou_threshold"] = iou_threshold
      additional_user_defined_metadata = (
          _coreml_utils._get_tc_version_info()
      )
      short_description = _coreml_utils._mlmodel_short_description(
          "Object Detector"
      )
  
      self.__proxy__.export_to_coreml(
          filename,
          short_description,
          additional_user_defined_metadata,
          options,
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import time
  from datetime import datetime
  import six
  import turicreate
  from turicreate.toolkits._model import Model
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits import _coreml_utils
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe, _numeric_param_check_range, _raise_error_if_not_iterable
  from turicreate.toolkits._main import ToolkitError
  from turicreate._deps.minimal_package import _minimal_package_import_check
  from ..None import _pre_trained_models
  from .._mps_utils import MpsGraphAPI, MpsGraphNetworkType
label_desc: Use image node_id 6 for calling the ObjectDetector obj's underlying member method code with example usage: obj.export_coreml(filename, include_non_maximum_suppression, iou_threshold, confidence_threshold) without return types