class_docstr: Base class for all loss functions.
docstr: Mean function.
  
  This is the inverse of the link function. Typically, a loss function takes as input the raw
  output of a model. In the case of classification, the raw output would be logits. The mean
  function can be used to convert the raw output into a value that makes sense to the user,
  such as a probability.
  
  Parameters
  ----------
  y_pred
      Raw prediction(s).
  
  Returns
  -------
  The adjusted prediction(s).
  
  References
  ----------
  [^1]: [Wikipedia section on link and mean function](https://www.wikiwand.com/en/Generalized_linear_model#/Link_function)
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code: def mean_func(self, y_pred):
      """Mean function.
  
      This is the inverse of the link function. Typically, a loss function takes as input the raw
      output of a model. In the case of classification, the raw output would be logits. The mean
      function can be used to convert the raw output into a value that makes sense to the user,
      such as a probability.
  
      Parameters
      ----------
      y_pred
          Raw prediction(s).
  
      Returns
      -------
      The adjusted prediction(s).
  
      References
      ----------
      [^1]: [Wikipedia section on link and mean function](https://www.wikiwand.com/en/Generalized_linear_model#/Link_function)
  
      """
  
imports: from __future__ import annotations
  import abc
  import typing
  import numpy
  from river import base, optim, utils
label_desc: Use image node_id 4 for calling the Loss obj's underlying member method code with example usage: obj.mean_func(y_pred) without return types