class_docstr: Base class to implement optimization objectives used in Stochastic Gradient Trees.
class_docstr_tok: ["Base","class","to","implement","optimization","objectives","used","in","Stochastic","Gradient","Trees","."]
docstr: Optionally apply some transformation to the value predicted by the tree before
  returning it.
  
  For instance, in classification, the softmax operation might be applied.
  
  Parameters
  ----------
  y
      Value to be transformed.
docstr_tok: ["Optionally","apply","some","transformation","to","the","value","predicted","by","the","tree","before","returning","it",".","For","instance",",","in","classification",",","the","softmax","operation","might","be","applied",".","Parameters","--","--","--","--","--","y","Value","to","be","transformed","."]
code: def transfer(self, y: float) -> float:
      """Optionally apply some transformation to the value predicted by the tree before
      returning it.
  
      For instance, in classification, the softmax operation might be applied.
  
      Parameters
      ----------
      y
          Value to be transformed.
      """
      return y
  
file_path: river/river/tree/losses.py
imports: from __future__ import annotations
  import abc
  import math
  from .utils import GradHess
label_desc: Use image node_id 2 for calling the Loss obj's underlying member method code with example usage: obj.transfer(y) and returns: y