class_docstr: A supervised transformer.
class_docstr_tok: ["A","supervised","transformer","."]
docstr: Update with a set of features `x` and a target `y`.
  
  Parameters
  ----------
  x
      A dictionary of features.
  y
      A target.
docstr_tok: ["Update","with","a","set","of","features","`","x","`","and","a","target","`","y","`",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features",".","y","A","target","."]
code: def learn_one(self, x: dict, y: base.typing.Target):
      """Update with a set of features `x` and a target `y`.
  
      Parameters
      ----------
      x
          A dictionary of features.
      y
          A target.
  
      """
      return
  
imports: from __future__ import annotations
  import abc
  import typing
  from river import base
label_desc: Use image node_id 2 for calling the SupervisedTransformer obj's underlying member method code with example usage: obj.learn_one(x, y) without return types