@@ -246,16 +246,13 @@ def apply(
246246
247247 Parameters
248248 ----------
249- forward: callable, no lambda
250- Function to transform the data in the forward pass.
251- For the adapter to be serializable, this function has to be serializable
252- as well (see Notes). Therefore, only proper functions and no lambda
253- functions should be used here.
254- inverse: callable, no lambda
255- Function to transform the data in the inverse pass.
256- For the adapter to be serializable, this function has to be serializable
257- as well (see Notes). Therefore, only proper functions and no lambda
258- functions should be used here.
249+ forward : str or np.ufunc
250+ The name of the NumPy function to use for the forward transformation.
251+ inverse : str or np.ufunc, optional
252+ The name of the NumPy function to use for the inverse transformation.
253+ By default, the inverse is inferred from the forward argument for supported methods.
254+ You can find the supported methods in
255+ :py:const:`~bayesflow.adapters.transforms.NumpyTransform.INVERSE_METHODS`.
259256 predicate : Predicate, optional
260257 Function that indicates which variables should be transformed.
261258 include : str or Sequence of str, optional
@@ -264,12 +261,6 @@ def apply(
264261 Names of variables to exclude from the transform.
265262 **kwargs : dict
266263 Additional keyword arguments passed to the transform.
267-
268- Notes
269- -----
270- Important: This is only serializable if the forward and inverse functions are serializable.
271- This most likely means you will have to pass the scope that the forward and inverse functions are contained in
272- to the `custom_objects` argument of the `deserialize` function when deserializing this class.
273264 """
274265 transform = FilterTransform (
275266 transform_constructor = NumpyTransform ,
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