"clearProperty('some.property')",
                                 ], [false, true]].combinations()
        indyStatus = enableIndy ? "with indy" : "without indy"
    }

    def "#setProperties is instrumented in dynamic Groovy #indyStatus"() {
        def configurationCache = newConfigurationCacheFixture()

        given:

        "/lib/groovy-2.0.5-indy.jar" | "2.0.5" | "org.codehaus.groovy:groovy:2.0.5:indy"
        "/lib/groovy-4.0.0-indy.jar" | "4.0.0" | "org.apache.groovy:groovy:4.0.0:indy"
        "/lib/groovy-4.1.0-indy.jar" | "4.1.0" | "org.apache.groovy:groovy:4.1.0:indy"
        "/lib/groovy-5.0.2-indy.jar" | "5.0.2" | "org.apache.groovy:groovy:5.0.2:indy"
    }

     * @return the list of test cases
     */
    abstract def testCases()

    /**
     * Produces a list of indy compilation modes. Each test case from {@link #testCases()} will run in every mode from the returned list.
     * @return the list of indy modes
     */
    def indyModes() {
        return [true, false]
    }

    final def testCasesWithIndyModes() {

        configurationCacheRun("-q", ":help")

        then:
        configurationCache.assertStateStored()

        where:
        enableIndy << [true, false]
        indyStatus = enableIndy ? "with indy" : "without indy"
    }

    // Lift the visibility of the method to make it available for the mixin
    @Override
    TestFile buildScript(@GroovyBuildScriptLanguage String script) {
        super.buildScript(script)
    }

                    }
                }
            }
        """

        file("buildSrc/build.gradle") << """
        compileGroovy {
            groovyOptions.optimizationOptions.indy = $enableIndy
        }
        """

        buildScript("""
            apply plugin: SomePlugin
        """)
    }

    abstract TestFile file(Object... path)

  const TorchAvgPoolData pool = GetTorchAvgPoolData(op);

  const auto values = GetPadOpPaddingValues(pool);

  llvm::SmallVector<int32_t> padding_vec(8, 0);  // NHWC
  for (auto [ind, val] : llvm::enumerate(values)) {
    padding_vec[ind + 2] = val;
  }

  auto padding_shape = RankedTensorType::get({4, 2}, builder.getI32Type());
  return DenseIntElementsAttr::get(padding_shape, padding_vec);
}