code: def clip_but_pass_gradient(input_, lower=-1.0, upper=1.0):
      clip_up = tf.cast(input_ > upper, tf.float32)
      clip_low = tf.cast(input_ < lower, tf.float32)
      return input_ + tf.stop_gradient(
          (upper - input_) * clip_up + (lower - input_) * clip_low
      )
  
imports: import tensorflow
  import numpy
  from gym.spaces import Box
  from stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy
  from stable_baselines.common.tf_layers import mlp
label_desc: Use image node_id 3 for calling a global function with example usage: clip_but_pass_gradient(input_, lower, upper) and returns: unknown