code: def load_mercari_price_prediction(train_path, test_path):
      train_df = pd.read_csv(train_path, sep="\t")
      test_df = pd.read_csv(test_path, sep="\t")
  
      train_cat1 = []
      train_cat2 = []
      train_cat3 = []
  
      test_cat1 = []
      test_cat2 = []
      test_cat3 = []
  
      for ele in train_df["category_name"]:
          if isinstance(ele, str):
              categories = ele.split("/", 2)
              train_cat1.append(categories[0])
              train_cat2.append(categories[1])
              train_cat3.append(categories[2])
          else:
              train_cat1.append(None)
              train_cat2.append(None)
              train_cat3.append(None)
  
      for ele in test_df["category_name"]:
          if isinstance(ele, str):
              categories = ele.split("/", 2)
              test_cat1.append(categories[0])
              test_cat2.append(categories[1])
              test_cat3.append(categories[2])
          else:
              test_cat1.append(None)
              test_cat2.append(None)
              test_cat3.append(None)
  
      # Convert to log(1 + x)
      train_df.loc[:, "price"] = np.log(train_df["price"] + 1)
      # train_df = train_df.drop('category_name', axis=1)
      train_df["cat1"] = train_cat1
      train_df["cat2"] = train_cat2
      train_df["cat3"] = train_cat3
  
      # test_df = test_df.drop('category_name', axis=1)
      test_df["cat1"] = test_cat1
      test_df["cat2"] = test_cat2
      test_df["cat3"] = test_cat3
  
      label_column = "price"
      ignore_columns = ["train_id"]
      feature_columns = []
      for column in sorted(train_df.columns):
          if column != label_column and column not in ignore_columns:
              feature_columns.append(column)
      train_df = train_df[feature_columns + [label_column]]
      test_df = test_df[feature_columns]
      return train_df, test_df, label_column
  
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 5 for calling a global function with example usage: load_mercari_price_prediction(train_path, test_path) and returns: train_df,  test_df,  label_column