class_docstr: Creates the graph for k-means clustering.
docstr: Computes distance between each input and each cluster center.
  
  Args:
    inputs: list of input Tensors.
    clusters: cluster Tensor.
    distance_metric: distance metric used for clustering
  
  Returns:
    list of Tensors, where each element corresponds to each element in inputs.
    The value is the distance of each row to all the cluster centers.
    Currently only Euclidean distance and cosine distance are supported.
docstr_tok: ["Computes","distance","between","each","input","and","each","cluster","center",".","Args",":","inputs",":","list","of","input","Tensors",".","clusters",":","cluster","Tensor",".","distance_metric",":","distance","metric","used","for","clustering","Returns",":","list","of","Tensors",",","where","each","element","corresponds","to","each","element","in","inputs",".","The","value","is","the","distance","of","each","row","to","all","the","cluster","centers",".","Currently","only","Euclidean","distance","and","cosine","distance","are","supported","."]
code: def _distance_graph(cls, inputs, clusters, distance_metric):
      """Computes distance between each input and each cluster center.
  
      Args:
        inputs: list of input Tensors.
        clusters: cluster Tensor.
        distance_metric: distance metric used for clustering
  
      Returns:
        list of Tensors, where each element corresponds to each element in inputs.
        The value is the distance of each row to all the cluster centers.
        Currently only Euclidean distance and cosine distance are supported.
      """
      assert isinstance(inputs, list)
      if distance_metric == SQUARED_EUCLIDEAN_DISTANCE:
          return cls._compute_euclidean_distance(inputs, clusters)
      elif distance_metric == COSINE_DISTANCE:
          return cls._compute_cosine_distance(
              inputs, clusters, inputs_normalized=True
          )
      else:
          assert False, str(distance_metric)
  
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 2 for calling the KMeans obj's underlying member method code with example usage: obj._distance_graph(cls, inputs, clusters, distance_metric) and returns: cls, cls