code: def load_price_of_books(train_path, test_path):
      train_df = pd.read_excel(train_path, engine="openpyxl")
      test_df = pd.read_excel(test_path, engine="openpyxl")
      # Convert Reviews
      train_df.loc[:, "Reviews"] = pd.to_numeric(
          train_df["Reviews"].apply(
              lambda ele: ele[: -len(" out of 5 stars")]
          )
      )
      test_df.loc[:, "Reviews"] = pd.to_numeric(
          test_df["Reviews"].apply(
              lambda ele: ele[: -len(" out of 5 stars")]
          )
      )
      # Convert Ratings
      train_df.loc[:, "Ratings"] = pd.to_numeric(
          train_df["Ratings"].apply(
              lambda ele: ele.replace(",", "")[
                  : -len(" customer reviews")
              ]
          )
      )
      test_df.loc[:, "Ratings"] = pd.to_numeric(
          test_df["Ratings"].apply(
              lambda ele: ele.replace(",", "")[
                  : -len(" customer reviews")
              ]
          )
      )
      # Convert Price to log scale
      train_df.loc[:, "Price"] = np.log10(train_df["Price"] + 1)
      return train_df, test_df, "Price"
  
file_path: autogluon/examples/automm/text_prediction/run_competition.py
imports: import os
  import json
  import argparse
  import pandas
  import numpy
  import random
  from autogluon.tabular import TabularPredictor
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
label_desc: Use image node_id 3 for calling a global function with example usage: load_price_of_books(train_path, test_path) and returns: train_df,  test_df,  str