docstr: Adjust the vectors for words in the vocabulary.
  
  The adjustment composes the trained full-word-token vectors with
  the vectors of the subword ngrams, matching the Facebook reference
  implementation behavior.
docstr_tok: ["Adjust","the","vectors","for","words","in","the","vocabulary",".","The","adjustment","composes","the","trained","full-word-token","vectors","with","the","vectors","of","the","subword","ngrams",",","matching","the","Facebook","reference","implementation","behavior","."]
code: def adjust_vectors(self):
      """Adjust the vectors for words in the vocabulary.
  
      The adjustment composes the trained full-word-token vectors with
      the vectors of the subword ngrams, matching the Facebook reference
      implementation behavior.
  
      """
      if self.bucket == 0:
          self.vectors = self.vectors_vocab  # no ngrams influence
          return
  
      self.vectors = self.vectors_vocab[:].copy()
      for i, _ in enumerate(self.index_to_key):
          ngram_buckets = self.buckets_word[i]
          for nh in ngram_buckets:
              self.vectors[i] += self.vectors_ngrams[nh]
          self.vectors[i] /= len(ngram_buckets) + 1
  
imports: import logging
  import numpy
  from numpy import ones, vstack, float32
  import gensim.models._fasttext_bin
  from gensim.models.word2vec import Word2Vec
  from gensim.models.keyedvectors import KeyedVectors, prep_vectors
  from gensim import utils
  from gensim.utils import deprecated
  from gensim.models import keyedvectors
label_desc: Use image node_id 11 for calling the FastTextKeyedVectors obj's underlying member method code with example usage: obj.adjust_vectors() without return types