class_docstr: Defines the FastText embedding technique.
  
  SEE: https://radimrehurek.com/gensim/models/fasttext.html
  
  The RDF2Vec implementation of FastText does not consider the min_n and
  max_n parameters for n_gram splitting.
  
  This implementation for RDF2Vec computes ngrams for walks only by splitting
  (by their symbol "#") the URIs of subjects and predicates. Indeed, objects
  being encoded in MD5, splitting in ngrams does not make sense.
  
  It is likely that you want to provide another split strategy for the
  calculation of the n-grams of the entities. If this is the case, provide
  your own compute_ngrams_bytes function to FastText.
  
  Attributes:
      _model: The gensim.models.word2vec model.
          Defaults to None.
      kwargs: The keyword arguments dictionary.
          Defaults to { bucket=2000000, min_count=0, max_n=0, min_n=0,
              negative=20, vector_size=500 }
      func_computing_ngrams: The function to call for the computation of
          ngrams. In case of reimplementation, it is important to respect the
          signature imposed by gensim:
          func(entity: str, minn: int = 0, maxn: int = 0) -> List[bytes]
          Defaults to compute_ngrams_bytes
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docstr: The features vector of the provided entities.
  
      Args:
          entities: The entities including test entities to create the
          embeddings. Since RDF2Vec is unsupervised, there is no label
          leakage.
  
  Returns:
      The features vector of the provided entities.
docstr_tok: ["The","features","vector","of","the","provided","entities",".","Args",":","entities",":","The","entities","including","test","entities","to","create","the","embeddings",".","Since","RDF2Vec","is","unsupervised",",","there","is","no","label","leakage",".","Returns",":","The","features","vector","of","the","provided","entities","."]
code: def transform(self, entities: Entities) -> Embeddings:
      """The features vector of the provided entities.
  
          Args:
              entities: The entities including test entities to create the
              embeddings. Since RDF2Vec is unsupervised, there is no label
              leakage.
  
      Returns:
          The features vector of the provided entities.
  
      """
      if not all([entity in self._model.wv for entity in entities]):
          raise ValueError(
              "The entities must have been provided to fit() first "
              "before they can be transformed into a numerical vector."
          )
      return [self._model.wv.get_vector(entity) for entity in entities]
  
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 3 for calling the FastText obj's underlying member method code with example usage: obj.transform(entities) and returns: unknown