code: def run_pipeline_with_specific_evaluation_mode(
      train_data: InputData, test_data: InputData, mode: str = None
  ):
      """
      Runs the example with 3-node pipeline.
      :param train_data: train data for pipeline training
      :param test_data: test data for pipeline training
      :param mode: pass gpu flag to make gpu evaluation
      """
      problem = "classification"
  
      if mode == "gpu":
          baseline_model = Fedot(problem=problem, preset="gpu")
      else:
          baseline_model = Fedot(problem=problem)
  
      preset_pipeline = classification_svc_complex_pipeline()
  
      start = datetime.now()
      baseline_model.fit(
          features=train_data,
          target="target",
          predefined_model=preset_pipeline,
      )
      finish = datetime.now() - start
      print(f"Completed in: {finish}")
  
      baseline_model.predict(features=test_data)
      print(baseline_model.get_metrics())
  
file_path: FEDOT/examples/advanced/gpu_example.py
imports: from datetime import datetime
  from typing import Tuple
  from sklearn.datasets import make_moons
  from examples.simple.classification.classification_pipelines import classification_svc_complex_pipeline
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 2 for calling a global function with example usage: run_pipeline_with_specific_evaluation_mode(train_data, test_data, mode) without return types