docstr: apply the update using the eigen values of the stats
  
  :param eigen_list: ([TensorFlow Tensor]) The list of eigen values of the stats
  :return: ([TensorFlow Tensor]) update operations
docstr_tok: ["apply","the","update","using","the","eigen","values","of","the","stats",":","param","eigen_list",":","(","[","TensorFlow","Tensor","]",")","The","list","of","eigen","values","of","the","stats",":","return",":","(","[","TensorFlow","Tensor","]",")","update","operations"]
code: def apply_stats_eigen(self, eigen_list):
      """
      apply the update using the eigen values of the stats
  
      :param eigen_list: ([TensorFlow Tensor]) The list of eigen values of the stats
      :return: ([TensorFlow Tensor]) update operations
      """
      update_ops = []
      if self.verbose > 1:
          print(("updating %d eigenvalue/vectors" % len(eigen_list)))
      for _, (tensor, mark) in enumerate(
          zip(eigen_list, self.eigen_update_list)
      ):
          stats_eigen_var = self.eigen_reverse_lookup[mark]
          update_ops.append(
              tf.assign(stats_eigen_var, tensor, use_locking=True)
          )
  
      with tf.control_dependencies(update_ops):
          factor_step_op = tf.assign_add(self.factor_step, 1)
          update_ops.append(factor_step_op)
          if KFAC_DEBUG:
              update_ops.append(
                  tf.Print(
                      tf.constant(0.0),
                      [tf.convert_to_tensor("updated kfac factors")],
                  )
              )
      return update_ops
  
imports: import re
  from functools import reduce
  import tensorflow
  import numpy
  from stable_baselines.acktr.kfac_utils import detect_min_val, factor_reshape, gmatmul
label_desc: Use image node_id 10 for calling the KfacOptimizer obj's underlying member method code with example usage: obj.apply_stats_eigen(eigen_list) and returns: update_ops