code: def _save_to_stream(model, fout, fb_fasttext_parameters, encoding):
      """
      Saves word embeddings to binary stream `fout` using the Facebook's native fasttext `.bin` format.
  
      Parameters
      ----------
      fout: file name or writeable binary stream
          stream to which the word embeddings are saved
      model: gensim.models.fasttext.FastText
          the model that contains the word embeddings to save
      fb_fasttext_parameters: dictionary
          dictionary contain parameters containing `lr_update_rate`, `word_ngrams`
          unused by gensim implementation, so they have to be provided externally
      encoding: str
          encoding used in the output file
      """
  
      _sign_model(fout)
      _args_save(fout, model, fb_fasttext_parameters)
      _dict_save(fout, model, encoding)
      fout.write(
          struct.pack("@?", False)
      )  # Save 'quant_', which is False for unsupervised models
  
      # Save words and ngrams vectors
      _input_save(fout, model)
      fout.write(
          struct.pack("@?", False)
      )  # Save 'quot_', which is False for unsupervised models
  
      # Save output layers of the model
      _output_save(fout, model)
  
imports: import collections
  import gzip
  import io
  import logging
  import struct
  import numpy
label_desc: Use image node_id 16 for calling a global function with example usage: _save_to_stream(model, fout, fb_fasttext_parameters, encoding) without return types