code: def predict_model(model, data: pd.DataFrame) -> pd.DataFrame:
      """
      This function generates anomaly labels on using a trained model.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> anomaly = get_data('anomaly')
      >>> from pycaret.anomaly import *
      >>> exp_name = setup(data = anomaly)
      >>> knn = create_model('knn')
      >>> knn_predictions = predict_model(model = knn, data = unseen_data)
  
  
      model: scikit-learn compatible object
          Trained Model Object.
  
  
      data : pandas.DataFrame
          Shape (n_samples, n_features) where n_samples is the number of samples and
          n_features is the number of features.
  
  
      Returns:
          pandas.DataFrame
  
  
      Warnings
      --------
      - The behavior of the predict_model is changed in version 2.1 without backward compatibility.
        As such, the pipelines trained using the version (<= 2.0), may not work for inference
        with version >= 2.1. You can either retrain your models with a newer version or downgrade
        the version for inference.
  
  
      """
  
      experiment = _CURRENT_EXPERIMENT
      if experiment is None:
          experiment = _EXPERIMENT_CLASS()
  
      return experiment.predict_model(estimator=model, data=data)
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.anomaly.oop import AnomalyExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 6 for calling a global function with example usage: predict_model(model, data) and returns: experiment