code: def load_facebook_model(path, encoding="utf-8"):
      """Load the model from Facebook's native fasttext `.bin` output file.
  
      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 requires you to **provide the full path to the .bin file**.
      It effectively ignores the `.vec` output file, since it is redundant.
  
      This function uses the smart_open library to open the path.
      The path may be on a remote host (e.g. HTTP, S3, etc).
      It may also be gzip or bz2 compressed (i.e. end in `.bin.gz` or `.bin.bz2`).
      For details, see `<https://github.com/RaRe-Technologies/smart_open>`__.
  
      Parameters
      ----------
      model_file : str
          Path to the FastText output files.
          FastText outputs two model files - `/path/to/model.vec` and `/path/to/model.bin`
          Expected value for this example: `/path/to/model` or `/path/to/model.bin`,
          as Gensim requires only `.bin` file to the load entire fastText model.
      encoding : str, optional
          Specifies the file encoding.
  
      Examples
      --------
  
      Load, infer, continue training:
  
      .. sourcecode:: pycon
  
          >>> from gensim.test.utils import datapath
          >>>
          >>> cap_path = datapath("crime-and-punishment.bin")
          >>> fb_model = load_facebook_model(cap_path)
          >>>
          >>> 'landlord' in fb_model.wv.key_to_index  # Word is out of vocabulary
          False
          >>> oov_term = fb_model.wv['landlord']
          >>>
          >>> 'landlady' in fb_model.wv.key_to_index  # Word is in the vocabulary
          True
          >>> iv_term = fb_model.wv['landlady']
          >>>
          >>> new_sent = [['lord', 'of', 'the', 'rings'], ['lord', 'of', 'the', 'flies']]
          >>> fb_model.build_vocab(new_sent, update=True)
          >>> fb_model.train(sentences=new_sent, total_examples=len(new_sent), epochs=5)
  
      Returns
      -------
      gensim.models.fasttext.FastText
          The loaded model.
  
      See Also
      --------
      :func:`~gensim.models.fasttext.load_facebook_vectors` loads
      the word embeddings only.  Its faster, but does not enable you to continue
      training.
  
      """
      return _load_fasttext_format(
          path, encoding=encoding, full_model=True
      )
  
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 2 for calling a global function with example usage: load_facebook_model(path, encoding) and returns: _load_fasttext_format