code: import numpy as np
  from sklearn.pipeline import Pipeline
  
  from models.model import Model, ArrayLike
  from preprocess.report_data import ReportData
  from preprocess.report_data_d import ColName
  
  from training.description_classification.utils import load_svm, SVMPipeline
  
  
  class SVMDescriptionClf(Model[SVMPipeline]):
      """Complement Naive Bayes model for description classification."""
      _model: Pipeline
  
      def __init__(self):
          self._model = load_svm()
  
      def predict(self, X: ArrayLike) -> np.ndarray:
          """Predict the primary incident type of the given descriptions.
  
          Params:
              X: 1D array-like of descriptions to classify
  
          Returns:
              1D array of `IncidentType` predictions for the given descriptions.
          """
          predictions = self._model.predict(X)
          return np.array([prediction for prediction in predictions])
  
      def partial_fit(self, X: ArrayLike, y: ArrayLike, classes: ArrayLike = None) -> object:
          pass
  
  
  if __name__ == '__main__':
      clf = SVMDescriptionClf()
      df = ReportData().get_processed_data()
      print(clf.predict([df[ColName.DESC][0]]))
  
apis: numpy.array