class_docstr: Health Insurance Cross Sell Prediction Predict Health Insurance Owners' who will be interested in Vehicle
  Insurance https://www.kaggle.com/datasets/arashnic/imbalanced-data-practice.
class_docstr_tok: ["Health","Insurance","Cross","Sell","Prediction","Predict","Health","Insurance","Owners","'","who","will","be","interested","in","Vehicle","Insurance","https",":","\/\/www.kaggle.com\/datasets\/arashnic\/imbalanced-data-practice","."]
code: def transform_dataframe(
      self, dataframe: pd.DataFrame
  ) -> pd.DataFrame:
      df = super().transform_dataframe(dataframe)
      # Make image paths relative to dataset root directory
      df["image_path"] = df["image_path"].apply(
          lambda x: os.path.join(
              "Fast_Furious_Insured", "trainImages", os.path.basename(x)
          )
      )
      return df
  
imports: import os
  import pandas
  from ludwig.datasets.loaders.dataset_loader import DatasetLoader
label_desc: Use image node_id 1 for calling the InsuranceLiteLoader obj's underlying member method code with example usage: obj.transform_dataframe(dataframe) and returns: df