code: def create_dialogue_dataset(
      dialogues: List[List[Dict[str, Any]]]
  ) -> Dict[str, Dict[str, Any]]:
      """
      Creates a dialogue dataset starting from a set of dialogues. Each
      entry of the dataset contains the dialogue history and the system
      reply in response to that.
  
      Arguments
      ---------
      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.
      kwargs: any
          Additional arguments for the current function.
  
      Returns
      -------
      dataset: Dict[str, Dict[str, Any]]
          Dataset, keys are str, values are dictionaries containing the
          dialogue history and the system reply.
      """
  
      def create_dialogue_dataset_entry(
          turn: Dict[str, Any], history: List[str]
      ) -> Optional[Dict[str, Any]]:
          """
          Creates an entry if the current turn id is odd. An entry is
          composed of the history, which contains the previous turns
          of the current dialogue, and the reply of the system.
  
          Arguments
          ---------
          turn: dict, keys are str, values could be anything
              A dict containing, the dialogue id, the turn id, the sequence,
              and the mean length.
          replacements_path: str
              Path to TRADE file containing (from, to) pairs, one per line.
          kwargs: any
              Additional arguments for the current function.
  
          Returns
          -------
          entry: optional dict, keys are str, values could be anything
              Entry of the dialogue dataset. It is a dict containing the history
              of the dialogue, i.e. a list of turns, the reply of the system,
              i.e. a turn, and the mean length.
          """
  
          turn_idx = turn["turn_idx"]
          entry: Optional[Dict[str, Any]] = None
          if turn_idx % 2 == 0:
              # user turn, simply append it to the history
              user_seq: str = turn["sequence"]
              history.append(user_seq)
          elif turn_idx % 2 == 1:
              # system turn, create the dataset entry, and the append it to the history
              system_seq: str = turn["sequence"]
              history_mean_length = mean(
                  [len(turn) for turn in history]
              )
              entry = {
                  "history": history.copy(),
                  "reply": system_seq,
                  "length": history_mean_length + len(system_seq),
              }
              history.append(system_seq)
          return entry
  
      dataset: Dict[str, Dict[str, Any]] = {}
      for dialogue in tqdm(dialogues, desc="Creating dataset"):
          history: List[str] = []
          for turn in dialogue:
              # custom function to create a dataset entry
              dataset_entry = create_dialogue_dataset_entry(
                  turn, history
              )
              # custom function to create a dataset key
              key = create_entry_key(turn)
              if dataset_entry is not None:
                  dataset[key] = dataset_entry
      return dataset
  
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 14 for calling a global function with example usage: create_dialogue_dataset(dialogues) and returns: dataset, entry