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 the value of a given field.
  
  +-----------------------+----------------------------------------------+
  |      Field            | Description                                  |
  +=======================+==============================================+
  | batch_size            | Number of randomly chosen examples to use in |
  |                       | each training iteration.                     |
  +-----------------------+----------------------------------------------+
  | cluster_id            | Cluster assignment for each data point and   |
  |                       | Euclidean distance to the cluster center     |
  +-----------------------+----------------------------------------------+
  | cluster_info          | Cluster centers, sum of squared Euclidean    |
  |                       | distances from each cluster member to the    |
  |                       | assigned center, and the number of data      |
  |                       | points belonging to the cluster              |
  +-----------------------+----------------------------------------------+
  | features              | Names of feature columns                     |
  +-----------------------+----------------------------------------------+
  | max_iterations        | Maximum number of iterations to perform      |
  +-----------------------+----------------------------------------------+
  | method                | Algorithm used to train the model.           |
  +-----------------------+----------------------------------------------+
  | num_clusters          | Number of clusters                           |
  +-----------------------+----------------------------------------------+
  | num_examples          | Number of examples in the dataset            |
  +-----------------------+----------------------------------------------+
  | num_features          | Number of feature columns used               |
  +-----------------------+----------------------------------------------+
  | num_unpacked_features | Number of features unpacked from the         |
  |                       | feature columns                              |
  +-----------------------+----------------------------------------------+
  | training_iterations   | Total number of iterations performed         |
  +-----------------------+----------------------------------------------+
  | training_time         | Total time taken to cluster the data         |
  +-----------------------+----------------------------------------------+
  | unpacked_features     | Names of features unpacked from the          |
  |                       | feature columns                              |
  +-----------------------+----------------------------------------------+
  
  Parameters
  ----------
  field : str
      The name of the field to query.
  
  Returns
  -------
  out
      Value of the requested field
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code: def _get(self, field):
      """
      Return the value of a given field.
  
      +-----------------------+----------------------------------------------+
      |      Field            | Description                                  |
      +=======================+==============================================+
      | batch_size            | Number of randomly chosen examples to use in |
      |                       | each training iteration.                     |
      +-----------------------+----------------------------------------------+
      | cluster_id            | Cluster assignment for each data point and   |
      |                       | Euclidean distance to the cluster center     |
      +-----------------------+----------------------------------------------+
      | cluster_info          | Cluster centers, sum of squared Euclidean    |
      |                       | distances from each cluster member to the    |
      |                       | assigned center, and the number of data      |
      |                       | points belonging to the cluster              |
      +-----------------------+----------------------------------------------+
      | features              | Names of feature columns                     |
      +-----------------------+----------------------------------------------+
      | max_iterations        | Maximum number of iterations to perform      |
      +-----------------------+----------------------------------------------+
      | method                | Algorithm used to train the model.           |
      +-----------------------+----------------------------------------------+
      | num_clusters          | Number of clusters                           |
      +-----------------------+----------------------------------------------+
      | num_examples          | Number of examples in the dataset            |
      +-----------------------+----------------------------------------------+
      | num_features          | Number of feature columns used               |
      +-----------------------+----------------------------------------------+
      | num_unpacked_features | Number of features unpacked from the         |
      |                       | feature columns                              |
      +-----------------------+----------------------------------------------+
      | training_iterations   | Total number of iterations performed         |
      +-----------------------+----------------------------------------------+
      | training_time         | Total time taken to cluster the data         |
      +-----------------------+----------------------------------------------+
      | unpacked_features     | Names of features unpacked from the          |
      |                       | feature columns                              |
      +-----------------------+----------------------------------------------+
  
      Parameters
      ----------
      field : str
          The name of the field to query.
  
      Returns
      -------
      out
          Value of the requested field
      """
      opts = {
          "model": self.__proxy__,
          "model_name": self.__name__,
          "field": field,
      }
      response = _tc.extensions._kmeans.get_value(opts)
  
      return response["value"]
  
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 4 for calling the KmeansModel obj's underlying member method code with example usage: obj._get(field) and returns: response