code: def load_facebook_vectors(path, encoding="utf-8"):
      """Load word embeddings from a model saved in 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 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.
      For details, see `<https://github.com/RaRe-Technologies/smart_open>`__.
  
      Parameters
      ----------
      path : str
          The location of the model file.
      encoding : str, optional
          Specifies the file encoding.
  
      Returns
      -------
      gensim.models.fasttext.FastTextKeyedVectors
          The word embeddings.
  
      Examples
      --------
  
      Load and infer:
  
          >>> from gensim.test.utils import datapath
          >>>
          >>> cap_path = datapath("crime-and-punishment.bin")
          >>> fbkv = load_facebook_vectors(cap_path)
          >>>
          >>> 'landlord' in fbkv.key_to_index  # Word is out of vocabulary
          False
          >>> oov_vector = fbkv['landlord']
          >>>
          >>> 'landlady' in fbkv.key_to_index  # Word is in the vocabulary
          True
          >>> iv_vector = fbkv['landlady']
  
      See Also
      --------
      :func:`~gensim.models.fasttext.load_facebook_model` loads
      the full model, not just word embeddings, and enables you to continue
      model training.
  
      """
      full_model = _load_fasttext_format(
          path, encoding=encoding, full_model=False
      )
      return full_model.wv
  
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 3 for calling a global function with example usage: load_facebook_vectors(path, encoding) and returns: full_model