class_docstr: Takes a list of text file as input and binarizes them in memory at
  instantiation. Original lines are also kept in memory
class_docstr_tok: ["Takes","a","list","of","text","file","as","input","and","binarizes","them","in","memory","at","instantiation",".","Original","lines","are","also","kept","in","memory"]
code: def read_data(self, paths, dictionary):
      for path in paths:
          with open(path, "r") as f:
              file_lines = []
              file_tokens_list = []
              file_sizes = []
              for line in f:
                  file_lines.append(line.strip("\n"))
                  tokens = dictionary.encode_line(
                      line,
                      add_if_not_exist=False,
                      append_eos=self.append_eos,
                      reverse_order=self.reverse_order,
                  )
                  file_tokens_list.append(tokens)
                  file_sizes.append(len(tokens))
              self.lines.append(file_lines)
              self.tokens_list.append(file_tokens_list)
              self.sizes.append(file_sizes)
      # Zip all sentences for each sample together
      self.lines = list(zip(*self.lines))
      self.tokens_list = list(zip(*self.tokens_list))
      # Sum sentence sizes for each sample
      self.sizes = np.asarray(self.sizes).sum(axis=0)
  
file_path: translate/pytorch_translate/research/multisource/multisource_data.py
imports: import numpy
  import torch
  from fairseq import data
label_desc: Use image node_id 1 for calling the IndexedRawTextMultisentDataset obj's underlying member method code with example usage: obj.read_data(paths, dictionary) without return types