code: def apply_model_to_data(model: Pipeline, data_path: str):
      df, file_path = create_multi_clf_examples_from_excel(
          data_path, return_df=True
      )
      dataset_to_apply = InputData.from_csv(
          file_path, target_columns=None
      )
      evo_predicted = model.predict(dataset_to_apply)
      df["forecast"] = probs_to_labels(evo_predicted.predict)
      return df
  
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 3 for calling a global function with example usage: apply_model_to_data(model, data_path) and returns: df