docstr: Reimplementation of the ft_ngram_hahes method of gensim. This
  overwrite is needed to call our compute_ngrams_bytes method.
  
  Args:
       entity: The entity to hash the ngrams.
       minn: Minimum length of char n-grams to be used for training
          entity representations.
          Defaults to 0.
       maxn: Maximum length of char n-grams to be used for training
          entity representations.
          Defaults to 0.
       num_buckets: Character ngrams are hashed into a fixed number of
          buckets, in order to limit the memory usage of the model.
          Defaults to 2000000.
  
   Returns:
       The ngrams hashes.
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code: def ft_ngram_hashes(
      self,
      entity: str,
      minn: int = 0,
      maxn: int = 0,
      num_buckets: int = 2000000,
  ) -> List[Any]:
      """Reimplementation of the ft_ngram_hahes method of gensim. This
      overwrite is needed to call our compute_ngrams_bytes method.
  
      Args:
           entity: The entity to hash the ngrams.
           minn: Minimum length of char n-grams to be used for training
              entity representations.
              Defaults to 0.
           maxn: Maximum length of char n-grams to be used for training
              entity representations.
              Defaults to 0.
           num_buckets: Character ngrams are hashed into a fixed number of
              buckets, in order to limit the memory usage of the model.
              Defaults to 2000000.
  
       Returns:
           The ngrams hashes.
  
      """
      encoded_ngrams = self.func_computing_ngrams(entity, minn, maxn)
      hashes = [
          self.ft_hash_bytes(n) % num_buckets for n in encoded_ngrams
      ]
      return hashes
  
imports: from __future__ import annotations
  import re
  from typing import Any, List
  import attr
  import numpy
  from gensim.models.fasttext import FastText
  from gensim.models.fasttext import FastTextKeyedVectors
  from numpy import float32
  from numpy import ones
  from pyrdf2vec.embedders import Embedder
  from pyrdf2vec.typings import Embeddings, Entities, SWalk
label_desc: Use image node_id 6 for calling the RDFFastTextKeyedVectors obj's underlying member method code with example usage: obj.ft_ngram_hashes(entity, minn, maxn, num_buckets) and returns: hashes