code: def execute_pipeline_for_text_problem(train_data, test_data):
      node_text_clean = PipelineNode("text_clean")
      node_tfidf = PipelineNode("tfidf", nodes_from=[node_text_clean])
      model_node = PipelineNode("multinb", nodes_from=[node_tfidf])
      pipeline = Pipeline(model_node)
      pipeline.fit(train_data)
  
      predicted = pipeline.predict(test_data)
  
      roc_auc_metric = roc_auc(
          y_true=test_data.target, y_score=predicted.predict
      )
  
      return roc_auc_metric
  
file_path: FEDOT/cases/spam_detection.py
imports: import os
  from sklearn.metrics import roc_auc_score
  from cases.dataset_preparation import unpack_archived_data
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: execute_pipeline_for_text_problem(train_data, test_data) and returns: roc_auc_metric