code: def create_reviews_data(test_size):
      reviews_data = retrieve_dataset("reviews.json")
      papers = reviews_data["paper"]
      reviews = []
      evaluation = []
      for paper in papers:
          if paper["review"] is None or not paper["review"]:
              continue
          reviews.append(paper["review"][0]["text"])
          evaluation.append(paper["review"][0]["evaluation"])
      return train_test_split(
          reviews, evaluation, test_size=test_size, random_state=7
      )
  
imports: import numpy
  import pandas
  from sklearn import svm, ensemble, linear_model
  from sklearn.datasets import load_iris, load_boston
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
  from sklearn.pipeline import Pipeline
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, LinearRegression
  from sklearn.base import TransformerMixin
  from lightgbm import LGBMClassifier
  from xgboost import XGBClassifier
  from tensorflow import keras
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Dropout, Activation
  import torch
  import torch.nn
  import torch.nn.functional
  from pandas import read_csv
  from datasets import retrieve_dataset
label_desc: Use image node_id 27 for calling a global function with example usage: create_reviews_data(test_size) and returns: train_test_split