class_docstr: Creates the graph for k-means clustering.
docstr: Computes Euclidean distance between each input and each cluster center.
  
  Args:
    inputs: list of input Tensors.
    clusters: cluster Tensor.
  
  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.
docstr_tok: ["Computes","Euclidean","distance","between","each","input","and","each","cluster","center",".","Args",":","inputs",":","list","of","input","Tensors",".","clusters",":","cluster","Tensor",".","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","."]
code: def _compute_euclidean_distance(cls, inputs, clusters):
      """Computes Euclidean distance between each input and each cluster center.
  
      Args:
        inputs: list of input Tensors.
        clusters: cluster Tensor.
  
      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.
      """
      output = []
      for inp in inputs:
          with ops.colocate_with(inp, ignore_existing=True):
              # Computes Euclidean distance. Note the first and third terms are
              # broadcast additions.
              squared_distance = (
                  math_ops.reduce_sum(
                      math_ops.square(inp), 1, keepdims=True
                  )
                  - 2 * math_ops.matmul(inp, clusters, transpose_b=True)
                  + array_ops.transpose(
                      math_ops.reduce_sum(
                          math_ops.square(clusters), 1, keepdims=True
                      )
                  )
              )
              output.append(squared_distance)
  
      return output
  
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 3 for calling the KMeans obj's underlying member method code with example usage: obj._compute_euclidean_distance(cls, inputs, clusters) and returns: output