code: def redirected_relu6_grad(op, grad):
      assert op.type == "Relu6"
      x = op.inputs[0]
  
      # Compute ReLu gradient
      relu6_cond = tf.logical_or(x < 0.0, x > 6.0)
      relu_grad = tf.where(relu6_cond, tf.zeros_like(grad), grad)
  
      # Compute redirected gradient: where do we need to zero out incoming gradient
      # to prevent input going lower if its already negative, or going higher if
      # already bigger than 6?
      neg_pushing_lower = tf.logical_and(x < 0.0, grad > 0.0)
      pos_pushing_higher = tf.logical_and(x > 6.0, grad < 0.0)
      dir_filter = tf.logical_or(neg_pushing_lower, pos_pushing_higher)
      redirected_grad = tf.where(dir_filter, tf.zeros_like(grad), grad)
  
      # Ensure we have at least a rank 2 tensor, as we expect a batch dimension
      assert_op = tf.Assert(
          tf.greater(tf.rank(relu_grad), 1), [tf.rank(relu_grad)]
      )
      with tf.control_dependencies([assert_op]):
          # only use redirected gradient where nothing got through original gradient
          batch = tf.shape(relu_grad)[0]
          reshaped_relu_grad = tf.reshape(relu_grad, [batch, -1])
          relu_grad_mag = tf.norm(reshaped_relu_grad, axis=1)
      result_grad = tf.where(
          relu_grad_mag > 0.0, relu_grad, redirected_grad
      )
  
      global_step_t = tf.train.get_or_create_global_step()
      return_relu_grad = tf.greater(
          global_step_t, tf.constant(16, tf.int64)
      )
  
      return tf.where(return_relu_grad, relu_grad, result_grad)
  
label_desc: Use image node_id 2 for calling a global function with example usage: redirected_relu6_grad(op, grad) and returns: tf