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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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']