code: def register_transformer_smaller_training_vocab(self, **kw):
      if not isinstance(self.model, ReduceTransformerVocabMixin):
          log.warning(
              "Cannot reduce the transformer vocab: model is not supported."
          )
          self.disabled = True
          return
  
      embeddings = get_transformer_embeddings(self.model)
      if not embeddings:
          self.disabled = True
          log.warning(
              "Cannot reduce the transformer vocab: no transformer embeddings found."
          )
  
      max_context_length = max(emb.context_length for emb in embeddings)
      respect_document_boundaries = all(
          emb.respect_document_boundaries for emb in embeddings
      )
  
      tokens = list(
          filter(
              None,
              self.model.get_used_tokens(
                  self.corpus,
                  max_context_length,
                  respect_document_boundaries,
              ),
          )
      )
      for emb in embeddings:
          self.trainer.context_stack.enter_context(
              reduce_train_vocab(
                  model=emb.model,
                  tokenizer=emb.tokenizer,
                  texts=tokens,
                  optimizer=self.trainer.optimizer,
              )
          )
  
file_path: flair/flair/trainers/plugins/functional/reduce_transformer_vocab.py
imports: import logging
  from pathlib import Path
  from typing import List
  from transformer_smaller_training_vocab import reduce_train_vocab
  from flair.embeddings import Embeddings, StackedEmbeddings, TransformerEmbeddings
  from flair.models import FewshotClassifier
  from flair.nn import Model
  from flair.nn.model import ReduceTransformerVocabMixin
  from flair.trainers.plugins import TrainerPlugin
label_desc: Use image node_id 2 for calling the ReduceTransformerVocabPlugin obj's underlying member method code with example usage: obj.register_transformer_smaller_training_vocab() without return types