class_docstr: A transformer.
class_docstr_tok: ["A","transformer","."]
docstr: Update with a set of features `x`.
  
  A lot of transformers don't actually have to do anything during the `learn_one` step
  because they are stateless. For this reason the default behavior of this function is to do
  nothing. Transformers that however do something during the `learn_one` can override this
  method.
  
  Parameters
  ----------
  x
      A dictionary of features.
docstr_tok: ["Update","with","a","set","of","features","`","x","`",".","A","lot","of","transformers","do","n't","actually","have","to","do","anything","during","the","`","learn_one","`","step","because","they","are","stateless",".","For","this","reason","the","default","behavior","of","this","function","is","to","do","nothing",".","Transformers","that","however","do","something","during","the","`","learn_one","`","can","override","this","method",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features","."]
code: def learn_one(self, x: dict):
      """Update with a set of features `x`.
  
      A lot of transformers don't actually have to do anything during the `learn_one` step
      because they are stateless. For this reason the default behavior of this function is to do
      nothing. Transformers that however do something during the `learn_one` can override this
      method.
  
      Parameters
      ----------
      x
          A dictionary of features.
  
      """
      return
  
imports: from __future__ import annotations
  import abc
  import typing
  from river import base
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.learn_one(x) without return types