code: def parameter_change_mutation(
      pipeline: Pipeline,
      requirements,
      graph_gen_params,
      parameters,
      **kwargs,
  ) -> Pipeline:
      """
      This type of mutation is passed over all nodes and changes
      hyperparameters of the operations with probability - 'node mutation probability'
      which is initialised inside the function
      """
      node_mutation_probability = get_mutation_prob(
          mut_id=parameters.mutation_strength, node=pipeline.root_node
      )
      for node in pipeline.nodes:
          lagged = node.operation.metadata.id in (
              "lagged",
              "sparse_lagged",
              "exog_ts",
          )
          do_mutation = random() < (
              node_mutation_probability * (0.5 if lagged else 1)
          )
          if do_mutation:
              operation_name = node.operation.operation_type
              current_params = node.parameters
  
              # Perform specific change for particular parameter
              changer = ParametersChanger(
                  operation_name, current_params
              )
              try:
                  new_params = changer.get_new_operation_params()
                  if new_params is not None:
                      node.parameters = new_params
              except Exception as ex:
                  pipeline.log.error(ex)
      return pipeline
  
file_path: FEDOT/fedot/core/composer/gp_composer/specific_operators.py
imports: from random import choice, random
  from typing import List
  from golem.core.optimisers.genetic.operators.base_mutations import get_mutation_prob
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.tuning.hyperparams import ParametersChanger
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from fedot.core.repository.tasks import TaskTypesEnum
label_desc: Use image node_id 1 for calling a global function with example usage: parameter_change_mutation(pipeline, requirements, graph_gen_params, parameters) and returns: pipeline