class_docstr: A class to read TED talk dataset
      
class_docstr_tok: ["A","class","to","read","TED","talk","dataset"]
code: def read_aligned_corpus(self, split_type="train"):
      data_dict = defaultdict(list)
      iterable = []
      s_list = []
      t_list = []
  
      if self.zero_shot:
          if split_type == "train":
              iterable = zip(
                  self.lang_dict["source"], self.lang_dict["target"]
              )
          else:
              iterable = zip(
                  self.eval_lang_dict["source"],
                  self.eval_lang_dict["target"],
              )
  
      elif self.bilingual:
          iterable = itertools.product(
              self.lang_dict["source"], self.lang_dict["target"]
          )
  
      for s_lang, t_lang in iterable:
          if s_lang == t_lang:
              continue
          if self.corpus_type == "file":
              split_type_file_path = os.path.join(
                  self.corpus_path,
                  "all_talks_{}.tsv".format(split_type),
              )
              s_list, t_list = self.read_from_single_file(
                  split_type_file_path, s_lang=s_lang, t_lang=t_lang
              )
          data_dict["source"] += s_list
          data_dict["target"] += t_list
      new_data_dict = self.filter_text(data_dict)
      return new_data_dict
  
imports: import itertools
  import os
  import csv
  from collections import defaultdict
  from six.moves import zip
  import io
  import wget
  import sys
  from subprocess import check_call, check_output
label_desc: Use image node_id 8 for calling the MultiLingualAlignedCorpusReader obj's underlying member method code with example usage: obj.read_aligned_corpus(split_type) and returns: new_data_dict