class_docstr: Class for the hyperparameters changing in the operation
  
  :param operation_name: name of operation to get hyperparameters for
  :param current_params: current parameters value
class_docstr_tok: ["Class","for","the","hyperparameters","changing","in","the","operation",":","param","operation_name",":","name","of","operation","to","get","hyperparameters","for",":","param","current_params",":","current","parameters","value"]
docstr: Function return a dictionary with new parameters values 
docstr_tok: ["Function","return","a","dictionary","with","new","parameters","values"]
code: def get_new_operation_params(self):
      """Function return a dictionary with new parameters values"""
  
      # Get available parameters for operation
      params_list = PipelineSearchSpace().get_parameters_for_operation(
          self.operation_name
      )
  
      if not params_list:
          params_dict = None
      else:
          # Get new values for all parameters
          params_dict = self.new_params_dict(params_list)
  
      return params_dict
  
file_path: FEDOT/fedot/core/pipelines/tuning/hyperparams.py
imports: import random
  import numpy
  from golem.core.log import default_log
  from golem.core.tuning.hyperopt_tuner import get_parameter_hyperopt_space
  from hyperopt.pyll.stochastic import sample
  from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
label_desc: Use image node_id 2 for calling the ParametersChanger obj's underlying member method code with example usage: obj.get_new_operation_params() and returns: params_dict