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: Randomly selects a parameter value from a specified range 
docstr_tok: ["Randomly","selects","a","parameter","value","from","a","specified","range"]
code: def _random_change(parameter_name, **kwargs):
      """Randomly selects a parameter value from a specified range"""
  
      space = get_parameter_hyperopt_space(
          PipelineSearchSpace(),
          operation_name=kwargs["operation_name"],
          parameter_name=parameter_name,
          label=parameter_name,
      )
      # Randomly choose new value
      rng = np.random.default_rng(
          random.randint(0, np.iinfo(np.int32).max)
      )
      new_value = hp_sample(space, rng=rng)
      if isinstance(new_value, np.ndarray) and new_value.size == 1:
          if len(new_value.shape) == 0:
              new_value = new_value.item()
          else:
              new_value = new_value[0]
      return {parameter_name: new_value}
  
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 5 for calling the ParametersChanger obj's underlying member method code with example usage: obj._random_change(parameter_name) and returns: dict