class_docstr: K-means clustering model. A k-means model object contains the results of
  running kmeans clustering on a dataset.  Queryable fields include a cluster
  id for each vertex, as well as the centers of the clusters.
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.kmeans.create` to create an instance of this model. A
  detailed list of parameter options and code samples are available in the
  documentation for the create function.
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docstr: Return a structured description of the model. This includes (where
  relevant) the schema of the training data, description of the training
  data, training statistics, and model hyperparameters.
  
  Returns
  -------
  sections : list (of list of tuples)
      A list of summary sections.
        Each section is a list.
          Each item in a section list is a tuple of the form:
            ('<label>','<field>')
  section_titles: list
      A list of section titles.
        The order matches that of the 'sections' object.
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code: def _get_summary_struct(self):
      """
      Return a structured description of the model. This includes (where
      relevant) the schema of the training data, description of the training
      data, training statistics, and model hyperparameters.
  
      Returns
      -------
      sections : list (of list of tuples)
          A list of summary sections.
            Each section is a list.
              Each item in a section list is a tuple of the form:
                ('<label>','<field>')
      section_titles: list
          A list of section titles.
            The order matches that of the 'sections' object.
      """
      model_fields = [
          ("Number of clusters", "num_clusters"),
          ("Number of examples", "num_examples"),
          ("Number of feature columns", "num_features"),
          ("Number of unpacked features", "num_unpacked_features"),
          ("Row label name", "row_label_name"),
      ]
  
      training_fields = [
          ("Training method", "method"),
          ("Number of training iterations", "training_iterations"),
          ("Batch size", "batch_size"),
          ("Total training time (seconds)", "training_time"),
      ]
  
      section_titles = ["Schema", "Training Summary"]
  
      return ([model_fields, training_fields], section_titles)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import logging
  from array import array
  import turicreate
  from turicreate.toolkits._model import Model
  from turicreate.data_structures.sframe import SFrame
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits._private_utils import _validate_row_label
  from turicreate.toolkits._private_utils import _summarize_accessible_fields
  from turicreate.toolkits._main import ToolkitError
  from turicreate._cython.cy_server import QuietProgress
label_desc: Use image node_id 6 for calling the KmeansModel obj's underlying member method code with example usage: obj._get_summary_struct() and returns: list,  section_titles