enh: use the autograd_params to not take a gradient with respect to lambda

Author: jonas-eschle

src/zfit_physics/models/pdf_Ipatia2.py

@@ -263,6 +263,16 @@ def __init__(
                The default space is used for example in the sample method: if no
                sampling limits are given, the default space is used.
 
+               If the observables are binned and the model is unbinned, the
+               model will be a binned model, by wrapping the model in a
+               :py:class:`~zfit.pdf.BinnedFromUnbinnedPDF`, equivalent to
+               calling :py:meth:`~zfit.pdf.BasePDF.to_binned`.
+
+               If the observables are binned and the model is unbinned, the
+               model will be a binned model, by wrapping the model in a
+               :py:class:`~zfit.pdf.BinnedFromUnbinnedPDF`, equivalent to
+               calling :py:meth:`~zfit.pdf.BasePDF.to_binned`.
+
                The observables are not equal to the domain as it does not restrict or
                truncate the model outside this range. |@docend:pdf.init.obs|
             mu: Location parameter. Shifts the distribution left/right.
@@ -281,10 +291,13 @@ def __init__(
                ``ext_*`` methods and the ``counts`` (for binned PDFs). |@docend:pdf.init.extended|
             norm: |@doc:pdf.init.norm| Normalization of the PDF.
                By default, this is the same as the default space of the PDF. |@docend:pdf.init.norm|
-            name: |@doc:pdf.init.name| Human-readable name
+            name: |@doc:pdf.init.name| Name of the PDF.
+               Maybe has implications on the serialization and deserialization of the PDF.
+               For a human-readable name, use the label. |@docend:pdf.init.name|
+           label: |@doc:pdf.init.label| Human-readable name
                or label of
-               the PDF for better identification. |@docend:pdf.init.name|
-           label: |@doc:pdf.init.label| Label of the PDF, if None is given, it will be the name. |@docend:pdf.init.label|
+               the PDF for a better description, to be used with plots etc.
+               Has no programmatical functional purpose as identification. |@docend:pdf.init.label|
         """
         params = {
             "mu": mu,
@@ -297,10 +310,20 @@ def __init__(
             "alphar": alphar,
             "nr": nr,
         }
-        super().__init__(obs=obs, params=params, name=name, extended=extended, norm=norm, label=label)
+        autograd_params = set(params) - {"lambd"}
+        super().__init__(
+            obs=obs,
+            params=params,
+            name=name,
+            extended=extended,
+            norm=norm,
+            label=label,
+            autograd_params=autograd_params,
+        )
 
-    @zfit.supports()
-    def _unnormalized_pdf(self, x: tf.Tensor, params) -> tf.Tensor:
+    @zfit.supports(norm=False)
+    def _pdf(self, x: tf.Tensor, norm, params) -> tf.Tensor:
+        del norm
         x0 = x[0]
         mu = params["mu"]
         sigma = params["sigma"]
@@ -311,5 +334,4 @@ def _unnormalized_pdf(self, x: tf.Tensor, params) -> tf.Tensor:
         nl = params["nl"]
         alphar = params["alphar"]
         nr = params["nr"]
-
         return hypatia2_func(x0, mu, sigma, lambd, zeta, beta, alphal, nl, alphar, nr)