class_docstr: A transform that can operate on mini-batches.
class_docstr_tok: ["A","transform","that","can","operate","on","mini-batches","."]
docstr: Update with a mini-batch of features.
  
  A lot of transformers don't actually have to do anything during the `learn_many` 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_many` can override this
  method.
  
  Parameters
  ----------
  X
      A DataFrame of features.
docstr_tok: ["Update","with","a","mini-batch","of","features",".","A","lot","of","transformers","do","n't","actually","have","to","do","anything","during","the","`","learn_many","`","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_many","`","can","override","this","method",".","Parameters","--","--","--","--","--","X","A","DataFrame","of","features","."]
code: def learn_many(self, X: pd.DataFrame):
      """Update with a mini-batch of features.
  
      A lot of transformers don't actually have to do anything during the `learn_many` 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_many` can override this
      method.
  
      Parameters
      ----------
      X
          A DataFrame 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 MiniBatchTransformer obj's underlying member method code with example usage: obj.learn_many(X) without return types