Clarify fisher_type docstring

curvlinops/kfac.py

@@ -115,13 +115,14 @@ def __init__(
  from the parameters. Defaults to ``None``.
  seed: The seed for the random number generator used to draw labels
  from the model's predictive distribution. Defaults to ``2147483647``.
- fisher_type: The type of Fisher/GGN to approximate. If ``'type-2'``, the
- expectation over the model outputs is computed exactly by
- computing the backward pass for each output dimension. This is
- sometimes also called type-2 Fisher. If ``'mc'``, the expectation
- is approximated by sampling ``mc_samples`` labels from the model's
- predictive distribution. If ``'empirical'``, the empirical gradients
- are used which corresponds to the uncentered gradient covariance, or
+ fisher_type: The type of Fisher/GGN to approximate. If 'type-2', the
+ exact Hessian of the loss w.r.t. the model outputs is used. This
+ requires as many backward passes as the output dimension, i.e.
+ the number of classes for classification. This is sometimes also
+ called type-2 Fisher. If ``'mc'``, the expectation is approximated
+ by sampling ``mc_samples`` labels from the model's predictive
+ distribution. If ``'empirical'``, the empirical gradients are
+ used which corresponds to the uncentered gradient covariance, or
  the empirical Fisher. Defaults to ``'mc'``.
  mc_samples: The number of Monte-Carlo samples to use per data point.
  Will be ignored when ``fisher_type`` is not ``'mc'``.