code: def predict_model(
      estimator,
      data: Optional[pd.DataFrame] = None,
      probability_threshold: Optional[float] = None,
      encoded_labels: bool = False,
      raw_score: bool = False,
      round: int = 4,
      verbose: bool = True,
  ) -> pd.DataFrame:
      """
      This function predicts ``Label`` and ``Score`` (probability of predicted
      class) using a trained model. When ``data`` is None, it predicts label and
      score on the holdout set.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> juice = get_data('juice')
      >>> from pycaret.classification import *
      >>> exp_name = setup(data = juice,  target = 'Purchase')
      >>> 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.
  
  
      probability_threshold: float, default = None
          Threshold for converting predicted probability to class label.
          Unless this parameter is set, it will default to the value set
          during model creation. If that wasn't set, the default will be 0.5
          for all classifiers. Only applicable for binary classification.
  
  
      encoded_labels: bool, default = False
          When set to True, will return labels encoded as an integer.
  
  
      raw_score: bool, default = False
          When set to True, scores for all labels will be returned.
  
  
      round: int, default = 4
          Number of decimal places the metrics in the score grid will be rounded 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,
          probability_threshold=probability_threshold,
          encoded_labels=encoded_labels,
          raw_score=raw_score,
          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.classification.oop import ClassificationExperiment
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.loggers.base_logger import BaseLogger
  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 15 for calling a global function with example usage: predict_model(estimator, data, probability_threshold, encoded_labels, raw_score, round, verbose) and returns: experiment