parameters: (cls, inputs, clusters)
return_statement: return output
docstring: 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.
docstring_summary: Computes Euclidean distance between each input and each cluster center.
docstring_tokens: Computes Euclidean distance between each input and each cluster center .
function: 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
function_tokens: def _compute_euclidean_distance ( cls , inputs , clusters ) : 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