class_docstr: Base class of the embedding techniques.
docstr: Fits a model based on the provided corpus.
  
  Args:
      corpus: The corpus to fit the model.
  
  Returns:
      The fitted model according to an embedding technique.
  
  Raises:
      NotImplementedError: If this method is called, without having
          provided an implementation.
docstr_tok: ["Fits","a","model","based","on","the","provided","corpus",".","Args",":","corpus",":","The","corpus","to","fit","the","model",".","Returns",":","The","fitted","model","according","to","an","embedding","technique",".","Raises",":","NotImplementedError",":","If","this","method","is","called",",","without","having","provided","an","implementation","."]
code: def fit(
      self, corpus: List[List[SWalk]], is_update: bool = False
  ) -> Embedder:
      """Fits a model based on the provided corpus.
  
      Args:
          corpus: The corpus to fit the model.
  
      Returns:
          The fitted model according to an embedding technique.
  
      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 1 for calling the Embedder obj's underlying member method code with example usage: obj.fit(corpus, is_update) without return types