code: def predict_model(
      estimator,
      data: Optional[pd.DataFrame] = None,
      round: int = 4,
      verbose: bool = True,
  ) -> pd.DataFrame:
      """
      This function predicts ``Label`` using a trained model. When ``data`` is
      None, it predicts label on the holdout set.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> lr = create_model('lr')
      >>> pred_holdout = predict_model(lr)
      >>> pred_unseen = predict_model(lr, data = unseen_dataframe)
  
  
      estimator: scikit-learn compatible object
          Trained model object
  
  
      data : pandas.DataFrame
          Shape (n_samples, n_features). All features used during training
          must be available in the unseen dataset.
  
  
      round: int, default = 4
          Number of decimal places to round predictions to.
  
  
      verbose: bool, default = True
          When set to False, holdout score grid is not printed.
  
  
      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=estimator,
          data=data,
          round=round,
          verbose=verbose,
      )
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.regression.oop import RegressionExperiment
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 13 for calling a global function with example usage: predict_model(estimator, data, round, verbose) and returns: experiment