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: use a pre-fitted model to generate predictions
  @return: None
docstr_tok: ["use","a","pre-fitted","model","to","generate","predictions","@","return",":","None"]
code: def _get_predictions(self, **kwargs):
      """
      use a pre-fitted model to generate predictions
      @return: None
      """
      try:
          model = self._load_model(f=self.model_path)
          x_val = (
              self._prepare_predict_data()
          )  # the same is used for clustering
          y_pred = model.predict(x_val)
          y_pred = _reshape(y_pred)
          logger.info(
              f"predictions shape: {y_pred.shape} | shape len: {len(y_pred.shape)}"
          )
          logger.info(f"predict on targets: {self.target}")
          if not self.target:
              self.target = ["result"]
          df_pred = pd.DataFrame.from_dict(
              {
                  self.target[i]: y_pred[:, i]
                  if len(y_pred.shape) > 1
                  else y_pred
                  for i in range(len(self.target))
              }
          )
          return df_pred
  
      except Exception as e:
          logger.exception(f"Error while preparing predictions: {e}")
  
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 13 for calling the Igel obj's underlying member method code with example usage: obj._get_predictions() and returns: df_pred