text: import os
  import numpy as np
  import copy
  os.environ["CUDA_VISIBLE_DEVICES"]="-1" 
  import tensorflow as tf
  from learning.normalizer import Normalizer
  
  class TFNormalizer(Normalizer):
  
      def __init__(self, sess, scope, size, groups_ids=None, eps=0.02, clip=np.inf):
          self.sess = sess
          self.scope = scope
          super().__init__(size, groups_ids, eps, clip)
  
          with tf.variable_scope(self.scope):
              self._build_resource_tf()
          return
  
      # initialze count when loading saved values so that things don't change to quickly during updates
      def load(self):
          self.count = self.count_tf.eval()[0]
          self.mean = self.mean_tf.eval()
          self.std = self.std_tf.eval()
          self.mean_sq = self.calc_mean_sq(self.mean, self.std)
          return
  
      def update(self):
          super().update()
          self._update_resource_tf()
          return
  
      def set_mean_std(self, mean, std):
          super().set_mean_std(mean, std)
          self._update_resource_tf()
          return
  
      def normalize_tf(self, x):
          norm_x = (x - self.mean_tf) / self.std_tf
          norm_x = tf.clip_by_value(norm_x, -self.clip, self.clip)
          return norm_x
  
      def unnormalize_tf(self, norm_x):
          x = norm_x * self.std_tf + self.mean_tf
          return x
      
      def _build_resource_tf(self):
          self.count_tf = tf.get_variable(dtype=tf.int32, name='count', initializer=np.array([self.count], dtype=np.int32), trainable=False)
          self.mean_tf = tf.get_variable(dtype=tf.float32, name='mean', initializer=self.mean.astype(np.float32), trainable=False)
          self.std_tf = tf.get_variable(dtype=tf.float32, name='std', initializer=self.std.astype(np.float32), trainable=False)
          
          self.count_ph = tf.get_variable(dtype=tf.int32, name='count_ph', shape=[1])
          self.mean_ph = tf.get_variable(dtype=tf.float32, name='mean_ph', shape=self.mean.shape)
          self.std_ph = tf.get_variable(dtype=tf.float32, name='std_ph', shape=self.std.shape)
          
          self._update_op = tf.group(
              self.count_tf.assign(self.count_ph),
              self.mean_tf.assign(self.mean_ph),
              self.std_tf.assign(self.std_ph)
          )
          return
  
      def _update_resource_tf(self):
          feed = {
              self.count_ph: np.array([self.count], dtype=np.int32),
              self.mean_ph: self.mean,
              self.std_ph: self.std
          }
          self.sess.run(self._update_op, feed_dict=feed)
          return
  