class_docstr: The NearestNeighborsModel represents rows of an SFrame in a structure that
  is used to quickly and efficiently find the nearest neighbors of a query
  point.
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.nearest_neighbors.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 dictionary of statistics collected during creation of the
  model. These statistics are also available with the ``get`` method and
  are described in more detail in that method's documentation.
  
  Returns
  -------
  out : dict
      Dictionary of statistics compiled during creation of the
      NearestNeighborsModel.
  
  See Also
  --------
  summary
  
  Examples
  --------
  >>> sf = turicreate.SFrame({'label': range(3),
  ...                       'feature1': [0.98, 0.62, 0.11],
  ...                       'feature2': [0.69, 0.58, 0.36]})
  >>> model = turicreate.nearest_neighbors.create(sf, 'label')
  >>> model.training_stats()
  {'features': 'feature1, feature2',
   'label': 'label',
   'leaf_size': 1000,
   'num_examples': 3,
   'num_features': 2,
   'num_variables': 2,
   'training_time': 0.023223,
   'tree_depth': 1}
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code: def _training_stats(self):
      """
      Return a dictionary of statistics collected during creation of the
      model. These statistics are also available with the ``get`` method and
      are described in more detail in that method's documentation.
  
      Returns
      -------
      out : dict
          Dictionary of statistics compiled during creation of the
          NearestNeighborsModel.
  
      See Also
      --------
      summary
  
      Examples
      --------
      >>> sf = turicreate.SFrame({'label': range(3),
      ...                       'feature1': [0.98, 0.62, 0.11],
      ...                       'feature2': [0.69, 0.58, 0.36]})
      >>> model = turicreate.nearest_neighbors.create(sf, 'label')
      >>> model.training_stats()
      {'features': 'feature1, feature2',
       'label': 'label',
       'leaf_size': 1000,
       'num_examples': 3,
       'num_features': 2,
       'num_variables': 2,
       'training_time': 0.023223,
       'tree_depth': 1}
      """
  
      opts = {"model": self.__proxy__, "model_name": self.__name__}
      return _turicreate.extensions._nearest_neighbors.training_stats(
          opts
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._model import Model
  from turicreate.data_structures.sgraph import SGraph
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits._private_utils import _validate_row_label
  from turicreate.toolkits._private_utils import _validate_lists
  from turicreate.toolkits._private_utils import _robust_column_name
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits.distances._util import _convert_distance_names_to_functions
  from turicreate.toolkits.distances._util import _validate_composite_distance
  from turicreate.toolkits.distances._util import _scrub_composite_distance_features
  from turicreate.toolkits.distances._util import _get_composite_distance_features
  from turicreate._cython.cy_server import QuietProgress
  import array
  import copy
  import six
label_desc: Use image node_id 8 for calling the NearestNeighborsModel obj's underlying member method code with example usage: obj._training_stats() and returns: _turicreate