code: def batch_apply(self, batch, is_train=False, stats=None, **kwargs):
      bucket_size = len(batch)
      examples_with_terms = 0
  
      for i, (ex, _, _) in enumerate(batch):
          # Skip half examples to improve performance. This means we set
          # a hard limit for the `term_corpus_ratio` to 0.5, which is actually
          # quite high. TODO: We can add this (skipping examples) as an option
          if i % 2 == 0:
              original_src = ex["src"]
              augmented_example, is_match = self.apply(
                  ex, is_train, stats, **kwargs
              )
              if is_match and (
                  examples_with_terms
                  < bucket_size * self.term_corpus_ratio
              ):
                  examples_with_terms += 1
                  ex["src"] = augmented_example["src"]
              else:
                  ex["src"] = original_src
  
      logger.debug(
          f"Added terms to {examples_with_terms}/{bucket_size} examples"
      )
      return batch
  
imports: from onmt.utils.logging import logger
  from onmt.transforms import register_transform
  from .transform import Transform
  import spacy
  import ahocorasick
  import re
label_desc: Use image node_id 6 for calling the TerminologyTransform obj's underlying member method code with example usage: obj.batch_apply(batch, is_train, stats) and returns: batch