class_docstr: Defines the Word2Vec embedding technique.
  
  SEE: https://radimrehurek.com/gensim/models/word2vec.html
  
  Attributes:
      _model: The gensim.models.word2vec model.
          Defaults to None.
      kwargs: The keyword arguments dictionary.
          Defaults to { min_count=0 }.
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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.
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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]
  
file_path: pyRDF2Vec/pyrdf2vec/embedders/word2vec.py
imports: from __future__ import annotations
  from typing import List
  import attr
  from gensim.models.word2vec import Word2Vec
  from pyrdf2vec.embedders import Embedder
  from pyrdf2vec.typings import Embeddings, Entities, SWalk
label_desc: Use image node_id 3 for calling the Word2Vec obj's underlying member method code with example usage: obj.transform(entities) and returns: unknown