code: def run_pipeline_explain():
      # Specifying paths
      train_data_path = os.path.join(
          fedot_project_root(),
          "cases",
          "data",
          "cancer",
          "cancer_train.csv",
      )
      figure_path = "pipeline_explain_example.png"
  
      # Feature and class names for visualization
      feature_names = pd.read_csv(
          train_data_path, index_col=0, nrows=0
      ).columns.tolist()
      target_name = feature_names.pop()
      target = pd.read_csv(train_data_path, usecols=[target_name])[
          target_name
      ]
      class_names = target.unique().astype(str).tolist()
  
      # Data load
      train_data = InputData.from_csv(train_data_path)
  
      # Pipeline composition
      pipeline = classification_complex_pipeline()
  
      # Pipeline fitting
      pipeline.fit(train_data)
  
      # Pipeline explaining
      explainer = explain_pipeline(
          pipeline,
          data=train_data,
          method="surrogate_dt",
          visualization=True,
      )
  
      # Visualizing explanation and saving the plot
      print(f"Built surrogate model: {explainer.surrogate_str}")
      explainer.visualize(
          save_path=figure_path,
          dpi=200,
          feature_names=feature_names,
          class_names=class_names,
          precision=6,
      )
  
imports: import os
  import pandas
  from examples.simple.classification.classification_pipelines import classification_complex_pipeline
  from fedot.core.data.data import InputData
  from fedot.core.utils import fedot_project_root
  from fedot.explainability.explainers import explain_pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: run_pipeline_explain() without return types