class_docstr: Creates the graph for k-means clustering.
docstr: Computes cosine distance between each input and each cluster center.
  
  Args:
    inputs: list of input Tensor.
    clusters: cluster Tensor
    inputs_normalized: if True, it assumes that inp and clusters are
      normalized and computes the dot product which is equivalent to the
      cosine distance. Else it L2 normalizes the inputs first.
  
  Returns:
    list of Tensors, where each element corresponds to each element in inp.
    The value is the distance of each row to all the cluster centers.
docstr_tok: ["Computes","cosine","distance","between","each","input","and","each","cluster","center",".","Args",":","inputs",":","list","of","input","Tensor",".","clusters",":","cluster","Tensor","inputs_normalized",":","if","True",",","it","assumes","that","inp","and","clusters","are","normalized","and","computes","the","dot","product","which","is","equivalent","to","the","cosine","distance",".","Else","it","L2","normalizes","the","inputs","first",".","Returns",":","list","of","Tensors",",","where","each","element","corresponds","to","each","element","in","inp",".","The","value","is","the","distance","of","each","row","to","all","the","cluster","centers","."]
code: def _compute_cosine_distance(
      cls, inputs, clusters, inputs_normalized=True
  ):
      """Computes cosine distance between each input and each cluster center.
  
      Args:
        inputs: list of input Tensor.
        clusters: cluster Tensor
        inputs_normalized: if True, it assumes that inp and clusters are
          normalized and computes the dot product which is equivalent to the
          cosine distance. Else it L2 normalizes the inputs first.
  
      Returns:
        list of Tensors, where each element corresponds to each element in inp.
        The value is the distance of each row to all the cluster centers.
      """
      output = []
      if not inputs_normalized:
          with ops.colocate_with(clusters, ignore_existing=True):
              clusters = nn_impl.l2_normalize(clusters, axis=1)
      for inp in inputs:
          with ops.colocate_with(inp, ignore_existing=True):
              if not inputs_normalized:
                  inp = nn_impl.l2_normalize(inp, axis=1)
              output.append(
                  1 - math_ops.matmul(inp, clusters, transpose_b=True)
              )
      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 4 for calling the KMeans obj's underlying member method code with example usage: obj._compute_cosine_distance(cls, inputs, clusters, inputs_normalized) and returns: output