import tensorflow as tf

def huber_loss(y_true, y_pred):
  error = tf.keras.backend.abs(y_true - y_pred)
  cond  = error <= 1.0

  squared_loss = 0.5 * tf.keras.backend.square(error)
  linear_loss  = error - 0.5

  return tf.keras.backend.mean(
    tf.keras.backend.switch(cond, squared_loss, linear_loss))

def huber_loss(y_true, y_pred, is_weights):
  error = tf.keras.backend.abs((y_true - y_pred) * is_weights)
  cond  = error <= 1.0

  squared_loss = 0.5 * tf.keras.backend.square(error)
  linear_loss  = error - 0.5

  return tf.keras.backend.mean(
    tf.keras.backend.switch(cond, squared_loss, linear_loss))

def huber_loss(y_true, y_pred, is_weights):
  error = tf.keras.backend.abs(y_true - y_pred)
  cond  = error <= 1.0

  squared_loss = 0.5 * tf.keras.backend.square(error)
  linear_loss  = error - 0.5

  return tf.keras.backend.mean(
    is_weights * tf.keras.backend.switch(cond, squared_loss, linear_loss))