class_docstr: Creates the graph for k-means clustering.
class_docstr_tok: ["Creates","the","graph","for","k-means","clustering","."]
docstr: Returns True if clusters centers are kept normalized.
docstr_tok: ["Returns","True","if","clusters","centers","are","kept","normalized","."]
code: def _clusters_l2_normalized(self):
      """Returns True if clusters centers are kept normalized."""
      return self._distance_metric == COSINE_DISTANCE and (
          not self._use_mini_batch
          or self._mini_batch_steps_per_iteration > 1
      )
  
imports: from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_clustering_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_impl
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variable_v1
  from tensorflow.python.ops import while_loop
  from tensorflow.python.ops.embedding_ops import embedding_lookup
  from tensorflow.python.ops.gen_clustering_ops import *
label_desc: Use image node_id 6 for calling the KMeans obj's underlying member method code with example usage: obj._clusters_l2_normalized() and returns: unknown