code: def save_facebook_model(
      model, path, encoding="utf-8", lr_update_rate=100, word_ngrams=1
  ):
      """Saves word embeddings to the Facebook's native fasttext `.bin` format.
  
      Notes
      ------
      Facebook provides both `.vec` and `.bin` files with their modules.
      The former contains human-readable vectors.
      The latter contains machine-readable vectors along with other model parameters.
      **This function saves only the .bin file**.
  
      Parameters
      ----------
      model : gensim.models.fasttext.FastText
          FastText model to be saved.
      path : str
          Output path and filename (including `.bin` extension)
      encoding : str, optional
          Specifies the file encoding. Defaults to utf-8.
  
      lr_update_rate : int
          This parameter is used by Facebook fasttext tool, unused by Gensim.
          It defaults to Facebook fasttext default value `100`.
          In very rare circumstances you might wish to fiddle with it.
  
      word_ngrams : int
          This parameter is used by Facebook fasttext tool, unused by Gensim.
          It defaults to Facebook fasttext default value `1`.
          In very rare circumstances you might wish to fiddle with it.
  
      Returns
      -------
      None
      """
      fb_fasttext_parameters = {
          "lr_update_rate": lr_update_rate,
          "word_ngrams": word_ngrams,
      }
      gensim.models._fasttext_bin.save(
          model, path, fb_fasttext_parameters, encoding
      )
  
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 6 for calling a global function with example usage: save_facebook_model(model, path, encoding, lr_update_rate, word_ngrams) without return types