class_docstr: Igel is the base model to use the fit, evaluate and predict functions of the sklearn library
class_docstr_tok: ["Igel","is","the","base","model","to","use","the","fit",",","evaluate","and","predict","functions","of","the","sklearn","library"]
docstr: load a saved model from file
  @param f: path to model
  @return: loaded model
docstr_tok: ["load","a","saved","model","from","file","@","param","f",":","path","to","model","@","return",":","loaded","model"]
code: def _load_model(self, f: str = ""):
      """
      load a saved model from file
      @param f: path to model
      @return: loaded model
      """
      try:
          if not f:
              logger.info(f"result path: {self.results_path} ")
              logger.info(
                  f"loading model form {self.default_model_path} "
              )
              model = joblib.load(open(self.default_model_path, "rb"))
          else:
              logger.info(f"loading from {f}")
              model = joblib.load(open(f, "rb"))
          return model
      except FileNotFoundError:
          logger.error(f"File not found in {self.default_model_path} ")
  
imports: import json
  import logging
  import os
  import warnings
  import joblib
  import numpy
  import pandas
  from sklearn.model_selection import cross_validate, train_test_split
  from sklearn.multioutput import MultiOutputClassifier, MultiOutputRegressor
  from skl2onnx import convert_sklearn
  from skl2onnx.common.data_types import FloatTensorType
label_desc: Use image node_id 4 for calling the Igel obj's underlying member method code with example usage: obj._load_model(f) and returns: model