Use correct extensible predicate signatures in go docs

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: owen-mc

docs/codeql/codeql-language-guides/customizing-library-models-for-go.rst

@@ -58,8 +58,8 @@ The CodeQL library for Go analysis exposes the following extensible predicates:
 - ``sourceModel(package, type, subtypes, name, signature, ext, output, kind, provenance)``. This is used to model sources of potentially tainted data. The ``kind`` of the sources defined using this predicate determine which threat model they are associated with. Different threat models can be used to customize the sources used in an analysis. For more information, see ":ref:`Threat models <threat-models-go>`."
 - ``sinkModel(package, type, subtypes, name, signature, ext, input, kind, provenance)``. This is used to model sinks where tainted data may be used in a way that makes the code vulnerable.
 - ``summaryModel(package, type, subtypes, name, signature, ext, input, output, kind, provenance)``. This is used to model flow through elements.
-- ``barrierModel(namespace, type, subtypes, name, signature, ext, output, kind, provenance)``. This is used to model barriers, which are elements that stop the flow of taint.
-- ``barrierGuardModel(namespace, type, boolean subtypes, name, signature, ext, input, acceptingvalue, kind, provenance)``. This is used to model barrier guards, which are elements that can stop the flow of taint depending on a conditional check.
+- ``barrierModel(package, type, subtypes, name, signature, ext, output, kind, provenance)``. This is used to model barriers, which are elements that stop the flow of taint.
+- ``barrierGuardModel(package, type, subtypes, name, signature, ext, input, acceptingvalue, kind, provenance)``. This is used to model barrier guards, which are elements that can stop the flow of taint depending on a conditional check.
 - ``neutralModel(package, type, name, signature, kind, provenance)``. This is similar to a summary model but used to model the flow of values that have only a minor impact on the dataflow analysis. Manual neutral models (those with a provenance such as ``manual`` or ``ai-manual``) can be used to override generated summary models (those with a provenance such as ``df-generated``), so that the summary model will be ignored. Other than that, neutral models have no effect.
 
 The extensible predicates are populated using the models defined in data extension files.