code: def get_model(
      train_file_path: str,
      cur_lead_time: datetime.timedelta = timedelta(seconds=60),
  ):
      task = Task(task_type=TaskTypesEnum.classification)
      dataset_to_compose = InputData.from_csv(
          train_file_path, task=task
      )
  
      # the search of the models provided by the framework
      # that can be used as nodes in a pipeline for the selected task
      models_repo = OperationTypesRepository()
      available_model_types = models_repo.suitable_operation(
          task_type=task.task_type, tags=["simple"]
      )
  
      metric_function = ClassificationMetricsEnum.ROCAUC_penalty
  
      composer_requirements = PipelineComposerRequirements(
          primary=available_model_types,
          secondary=available_model_types,
          timeout=cur_lead_time,
      )
  
      # Create the genetic programming-based composer, that allow to find
      # the optimal structure of the composite model
      builder = (
          ComposerBuilder(task)
          .with_requirements(composer_requirements)
          .with_metrics(metric_function)
      )
      composer = builder.build()
  
      # run the search of best suitable model
      pipeline_evo_composed = composer.compose_pipeline(
          data=dataset_to_compose
      )
      pipeline_evo_composed.fit(input_data=dataset_to_compose)
  
      return pipeline_evo_composed
  
file_path: FEDOT/examples/simple/classification/multiclass_prediction.py
imports: import datetime
  import os
  import pandas
  from golem.utilities.requirements_notificator import warn_requirement
  from datetime import timedelta
  from fedot.core.composer.composer_builder import ComposerBuilder
  from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import ensure_directory_exists, fedot_project_root, get_split_data_paths, probs_to_labels, save_file_to_csv, split_data
  from sklearn.metrics import roc_auc_score
  from fedot.core.utils import set_random_seed
  from pathlib import Path
label_desc: Use image node_id 2 for calling a global function with example usage: get_model(train_file_path, cur_lead_time) and returns: pipeline_evo_composed