code: def load_dialogues(
      data_path: str,
      data_split: List[str],
      replacements: List[Tuple[str, str]],
  ) -> List[List[Dict[str, Any]]]:
      """
      Load dialogues from data_path, apply trade pre-processing, revert the
      subtokenization, and create a dictionary containing the dialogue id,
      the turn id, and the corrected sequence.
  
      Arguments
      ---------
      data_path: str
          Path to the json file containing the data.
      data_split: list of str
          List of string containing MultiWOZ 2.1 keys of the dialogues
          associated to a certain split (train, dev, test).
      replacements_path: str
          File containing (from, to) pairs, one per line.
  
      Returns
      -------
      dialogues: list of list of dict, keys are str, values could be anything
          List of dialogues. Each dialogue is a list of turns. Each turn is a
          dict containing dialogue_idx, turn_idx, and the corrected sequence.
      """
  
      def get_preprocessed_seq(
          original_seq: str, replacements: List[Tuple[str, str]]
      ) -> str:
          # apply trade normalization
          trade_seq = normalize(original_seq, replacements)
          # merge back subtokens
          sequence = invert_trade_subtokenization(
              original_seq, trade_seq
          )
          return sequence
  
      dialogues: List[List[Dict[str, Any]]] = []
  
      data = get_json_object(data_path)
  
      for dialogue_idx in tqdm(data_split, desc="Load Dialogues"):
          dial: List[Dict[str, Any]] = []
          original_dialogue: dict = data[dialogue_idx]
          turns: dict = original_dialogue["log"]
          for i, turn in enumerate(turns):
              sequence = get_preprocessed_seq(
                  turn["text"], replacements
              )
              to_save = {
                  "sequence": sequence,
                  "turn_idx": i,
                  "dialogue_idx": dialogue_idx,
              }
              dial.append(to_save)
          dialogues.append(dial)
      return dialogues
  
imports: from itertools import product
  from statistics import mean
  from typing import Any, Dict, List, Optional, Set, Tuple
  import json
  import logging
  import os
  import re
  import shutil
  from tqdm import tqdm
  from speechbrain.utils.data_utils import download_file
label_desc: Use image node_id 9 for calling a global function with example usage: load_dialogues(data_path, data_split, replacements) and returns: dialogues, sequence