docstr: Make underlying vectors match 'index_to_key' size; random-initialize any new rows.
docstr_tok: ["Make","underlying","vectors","match","'index_to_key","'","size",";","random-initialize","any","new","rows","."]
code: def resize_vectors(self, seed=0):
      """Make underlying vectors match 'index_to_key' size; random-initialize any new rows."""
  
      vocab_shape = (len(self.index_to_key), self.vector_size)
      # Unlike in superclass, 'vectors_vocab' array is primary with 'vectors' derived from it & ngrams
      self.vectors_vocab = prep_vectors(
          vocab_shape, prior_vectors=self.vectors_vocab, seed=seed
      )
      ngrams_shape = (self.bucket, self.vector_size)
      self.vectors_ngrams = prep_vectors(
          ngrams_shape, prior_vectors=self.vectors_ngrams, seed=seed + 1
      )
  
      self.allocate_vecattrs()
      self.norms = None
      self.recalc_char_ngram_buckets()  # ensure new words have precalc buckets
      self.adjust_vectors()
  
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 9 for calling the FastTextKeyedVectors obj's underlying member method code with example usage: obj.resize_vectors(seed) without return types