class_docstr: Base class of the embedding techniques.
docstr: Constructs a 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.
  
  Raises:
      NotImplementedError: If this method is called, without having
          provided an implementation.
docstr_tok: ["Constructs","a","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",".","Raises",":","NotImplementedError",":","If","this","method","is","called",",","without","having","provided","an","implementation","."]
code: def transform(self, entities: Entities) -> Embeddings:
      """Constructs a 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.
  
      Raises:
          NotImplementedError: If this method is called, without having
              provided an implementation.
  
      """
      raise NotImplementedError("This has to be implemented")
  
imports: from __future__ import annotations
  import abc
  from typing import List
  import attr
  from pyrdf2vec.typings import Embeddings, Entities, SWalk
label_desc: Use image node_id 2 for calling the Embedder obj's underlying member method code with example usage: obj.transform(entities) without return types