code: def predict(
      self, trained_operation, predict_data: InputData
  ) -> OutputData:
      n_classes = len(trained_operation.classes_)
      if self.output_mode in ["labels"]:
          prediction = trained_operation.predict(predict_data)
      elif (
          self.output_mode in ["probs", "full_probs", "default"]
          and predict_data.task.task_type
          is TaskTypesEnum.classification
      ):
          prediction = trained_operation.predict_proba(predict_data)
          if n_classes < 2:
              raise ValueError(
                  "Data set contain only 1 target class. Please reformat your data."
              )
          elif (
              n_classes == 2
              and self.output_mode != "full_probs"
              and len(prediction.shape) > 1
          ):
              prediction = prediction[:, 1]
      else:
          raise ValueError(
              f"Output mode {self.output_mode} is not supported"
          )
  
      return self._convert_to_output(prediction, predict_data)
  
imports: from typing import Optional
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy
  from fedot.core.operations.evaluation.operation_implementations.models.boostings_implementations import FedotCatBoostClassificationImplementation, FedotCatBoostRegressionImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.tasks import TaskTypesEnum
  from fedot.utilities.random import ImplementationRandomStateHandler
label_desc: Use image node_id 2 for calling the BoostingClassificationStrategy obj's underlying member method code with example usage: obj.predict(trained_operation, predict_data) and returns: self