docstr: 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.
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code: def predict_model(
      self,
      estimator,
      data: pd.DataFrame,
      ml_usecase: Optional[MLUsecase] = None,
  ) -> 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.
  
  
      """
      return super().predict_model(estimator, data, ml_usecase)
  
file_path: pycaret/pycaret/anomaly/oop.py
imports: from typing import Any, List, Optional, Tuple, Union
  import pandas
  from pycaret.containers.metrics.anomaly import get_all_metric_containers
  from pycaret.containers.models.anomaly import get_all_model_containers
  from pycaret.internal.logging import get_logger
  from pycaret.internal.pycaret_experiment.unsupervised_experiment import _UnsupervisedExperiment
  from pycaret.utils.generic import MLUsecase
label_desc: Use image node_id 5 for calling the AnomalyExperiment obj's underlying member method code with example usage: obj.predict_model(estimator, data, ml_usecase) and returns: super