code: def huber_loss(y_true, y_pred, clip_value):
      # Huber loss, see https://en.wikipedia.org/wiki/Huber_loss and
      # https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b
      # for details.
      assert clip_value > 0.0
  
      x = y_true - y_pred
      if np.isinf(clip_value):
          # Spacial case for infinity since Tensorflow does have problems
          # if we compare `K.abs(x) < np.inf`.
          return 0.5 * K.square(x)
  
      condition = K.abs(x) < clip_value
      squared_loss = 0.5 * K.square(x)
      linear_loss = clip_value * (K.abs(x) - 0.5 * clip_value)
      if K.backend() == "tensorflow":
          import tensorflow as tf
  
          if hasattr(tf, "select"):
              return tf.select(
                  condition, squared_loss, linear_loss
              )  # condition, true, false
          else:
              return tf.where(
                  condition, squared_loss, linear_loss
              )  # condition, true, false
      elif K.backend() == "theano":
          from theano import tensor as T
  
          return T.switch(condition, squared_loss, linear_loss)
      else:
          raise RuntimeError(
              'Unknown backend "{}".'.format(K.backend())
          )
  
imports: import numpy
  from keras.models import model_from_config, Sequential, Model, model_from_config
  import keras.optimizers
  import keras.backend
label_desc: Use image node_id 5 for calling a global function with example usage: huber_loss(y_true, y_pred, clip_value) and returns: unknown, tf, tf, T