code: def AmazonReviewPolarity(*args, **kwargs):
      """Defines AmazonReviewPolarity datasets.
  
      The labels include:
  
          - 0 : Negative polarity
          - 1 : Positive polarity
  
      Create supervised learning dataset: AmazonReviewPolarity
  
      Separately returns the training and test dataset
  
      Args:
          root: Directory where the datasets are saved. Default: ".data"
          ngrams: a contiguous sequence of n items from s string text.
              Default: 1
          vocab: Vocabulary used for dataset. If None, it will generate a new
              vocabulary based on the train data set.
          include_unk: include unknown token in the data (Default: False)
  
      Examples:
         >>> train_dataset, test_dataset = torchtext.datasets.AmazonReviewPolarity(ngrams=3)
  
      """
  
      return _setup_datasets(
          *(("AmazonReviewPolarity",) + args), **kwargs
      )
  
imports: import csv
  import io
  import logging
  import torch
  from torchtext.data.utils import get_tokenizer, ngrams_iterator
  from torchtext.utils import download_from_url, extract_archive
  from tqdm import tqdm
  from ..vocab import build_vocab_from_iterator, Vocab
label_desc: Use image node_id 10 for calling a global function with example usage: AmazonReviewPolarity() and returns: _setup_datasets