code: def check_train_sentences(
      src_path, tgt_path, direction, all_test_data, mess_up_train={}
  ):
      # src, tgt = direction.split('-')
      print(
          f"check training data for {direction} in {src_path} and {tgt_path}"
      )
      size = 0
      overlapped_size_counted_dup = 0
      if not os.path.exists(tgt_path) or not os.path.exists(src_path):
          return mess_up_train, size, overlapped_size_counted_dup
  
      with open(src_path) as f, open(tgt_path) as g:
          for src_line, tgt_line in zip(f, g):
              s = src_line.strip()
              t = tgt_line.strip()
              size += 1
              if s in all_test_data:
                  langs = mess_up_train.get(s, set())
                  langs.add(direction)
                  mess_up_train[s] = langs
                  overlapped_size_counted_dup += 1
              if t in all_test_data:
                  langs = mess_up_train.get(t, set())
                  langs.add(direction)
                  mess_up_train[t] = langs
                  overlapped_size_counted_dup += 1
      print(
          f"{direction}: size={size}, overlapped={overlapped_size_counted_dup}"
      )
      return mess_up_train, size, overlapped_size_counted_dup
  
file_path: fairseq/examples/multilingual/data_scripts/check_valid_test_overlaps.py
imports: import os
  import argparse
  import pandas
  import sys
label_desc: Use image node_id 5 for calling a global function with example usage: check_train_sentences(src_path, tgt_path, direction, all_test_data, mess_up_train) and returns: mess_up_train,  size,  overlapped_size_counted_dup, mess_up_train,  size,  overlapped_size_counted_dup