code: def setup_parameter_updates(parameters, parameter_update_vector):
      """
      Creates update rules for list of parameters from one vector.
      Function is useful in Conjugate Gradient or
      Levenberg-Marquardt optimization algorithms
  
      Parameters
      ----------
      parameters : list
          List of parameters.
  
      parameter_update_vector : Tensorfow varible
          Vector that contains updates for all parameters.
  
      Returns
      -------
      list
          List of updates separeted for each parameter.
      """
      updates = []
      start_position = 0
  
      for parameter in parameters:
          end_position = start_position + tf.size(parameter)
          new_parameter = tf.reshape(
              parameter_update_vector[start_position:end_position],
              parameter.shape,
          )
          updates.append((parameter, new_parameter))
          start_position = end_position
  
      return updates
  
file_path: neupy/neupy/utils/tf_utils.py
imports: from functools import wraps
  import numpy
  import tensorflow
  from neupy.utils.misc import as_tuple
  from neupy.utils.processing import asfloat
label_desc: Use image node_id 12 for calling a global function with example usage: setup_parameter_updates(parameters, parameter_update_vector) and returns: updates