docstr: Get `word` representations in vector space, as a 1D numpy array.
  
  Parameters
  ----------
  word : str
      Input word.
  norm : bool, optional
      If True, resulting vector will be L2-normalized (unit Euclidean length).
  
  Returns
  -------
  numpy.ndarray
      Vector representation of `word`.
  
  Raises
  ------
  KeyError
      If word and all its ngrams not in vocabulary.
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code: def get_vector(self, word, norm=False):
      """Get `word` representations in vector space, as a 1D numpy array.
  
      Parameters
      ----------
      word : str
          Input word.
      norm : bool, optional
          If True, resulting vector will be L2-normalized (unit Euclidean length).
  
      Returns
      -------
      numpy.ndarray
          Vector representation of `word`.
  
      Raises
      ------
      KeyError
          If word and all its ngrams not in vocabulary.
  
      """
      if word in self.key_to_index:
          return super(FastTextKeyedVectors, self).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 = ft_ngram_hashes(
              word, self.min_n, self.max_n, 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
              #
              logger.warning(
                  "could not extract any ngrams from %r, returning origin vector",
                  word,
              )
              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: 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 7 for calling the FastTextKeyedVectors obj's underlying member method code with example usage: obj.get_vector(word, norm) and returns: super, word_vec, unknown, unknown