parameters: (self, class_counts)
return_statement: return 1.0 - sum_squares / (sums * sums)
docstring: Calculate the Gini impurity.
  
      If c(i) denotes the i-th class count and c = sum_i c(i) then
        score = 1 - sum_i ( c(i) / c )^2
  
      Args:
        class_counts: A 2-D tensor of per-class counts, usually a slice or
          gather from variables.node_sums.
  
      Returns:
        A 1-D tensor of the Gini impurities for each row in the input.
docstring_summary: Calculate the Gini impurity.
docstring_tokens: Calculate the Gini impurity .
function: def _gini(self, class_counts):
      """Calculate the Gini impurity.
  
      If c(i) denotes the i-th class count and c = sum_i c(i) then
        score = 1 - sum_i ( c(i) / c )^2
  
      Args:
        class_counts: A 2-D tensor of per-class counts, usually a slice or
          gather from variables.node_sums.
  
      Returns:
        A 1-D tensor of the Gini impurities for each row in the input.
      """
      smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
      sums = math_ops.reduce_sum(smoothed, 1)
      sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)
  
      return 1.0 - sum_squares / (sums * sums)
function_tokens: def _gini ( self , class_counts ) : smoothed = 1.0 + array_ops . slice ( class_counts , [ 0 , 1 ] , [ - 1 , - 1 ] ) sums = math_ops . reduce_sum ( smoothed , 1 ) sum_squares = math_ops . reduce_sum ( math_ops . square ( smoothed ) , 1 ) return 1.0 - sum_squares / ( sums * sums )