class_docstr: Applies a transformer within different groups.
  
  This transformer allows you to split your data into groups and apply a transformer within each
  group. This happens in a streaming manner, which means that the groups are discovered online.
  A separate copy of the provided transformer is made whenever a new group appears. The groups
  are defined according to one or more keys.
  
  Parameters
  ----------
  transformer
  by
      The field on which to group the data. This can either by a single value, or a list of
      values.
class_docstr_tok: ["Applies","a","transformer","within","different","groups",".","This","transformer","allows","you","to","split","your","data","into","groups","and","apply","a","transformer","within","each","group",".","This","happens","in","a","streaming","manner",",","which","means","that","the","groups","are","discovered","online",".","A","separate","copy","of","the","provided","transformer","is","made","whenever","a","new","group","appears",".","The","groups","are","defined","according","to","one","or","more","keys",".","Parameters","--","--","--","--","--","transformer","by","The","field","on","which","to","group","the","data",".","This","can","either","by","a","single","value",",","or","a","list","of","values","."]
code: def learn_one(self, x):
      key = self._get_key(x)
      self.transformers[key].learn_one(x)
  
file_path: river/river/compose/grouper.py
imports: from __future__ import annotations
  import collections
  import copy
  import functools
  from river import base
label_desc: Use image node_id 3 for calling the Grouper obj's underlying member method code with example usage: obj.learn_one(x) without return types