code: def get_vector(self, word, norm=False):
      if word in self.key_to_index:
          return super().get_vector(word, norm=norm)
      elif self.bucket == 0:
          raise KeyError(
              "cannot calculate vector for OOV word without ngrams"
          )
      else:
          word_vec = np.zeros(
              self.vectors_ngrams.shape[1], dtype=np.float32
          )
          ngram_weights = self.vectors_ngrams
          ngram_hashes = self.ft_ngram_hashes(word, 0, 0, self.bucket)
          if len(ngram_hashes) == 0:
              #
              # If it is impossible to extract _any_ ngrams from the input
              # word, then the best we can do is return a vector that points
              # to the origin.  The reference FB implementation does this,
              # too.
              #
              # https://github.com/RaRe-Technologies/gensim/issues/2402
              #
              return word_vec
          for nh in ngram_hashes:
              word_vec += ngram_weights[nh]
          if norm:
              return word_vec / np.linalg.norm(word_vec)
          else:
              return word_vec / len(ngram_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 2 for calling the RDFFastTextKeyedVectors obj's underlying member method code with example usage: obj.get_vector(word, norm) and returns: super, word_vec, unknown, unknown