firDiagnostic.c.map { coneKotlinType ->
                firSymbolBuilder.typeBuilder.buildKtType(coneKotlinType)
            },
            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }
    add(FirErrors.AMBIGUOUS_SUPER) { firDiagnostic ->
        AmbiguousSuperImpl(
            firDiagnostic.a.map { coneKotlinType ->

     * provided, an empty map is returned defensively so that [toTypeArgumentsMapping] doesn't conjure any error types. If you want to map
     * too few type arguments meaningfully, please provide filler types explicitly.
     */
    private fun toTypeArgumentsMapping(
        typeArguments: List<FirTypeProjection>,
        partiallyAppliedSymbol: KtPartiallyAppliedSymbol<*, *>
    ): Map<KtTypeParameterSymbol, KtType> {

            testJvmVendorParameter,
            JvmVendor.openjdk.name,
            display = ParameterDisplay.PROMPT,
            description = "Java vendor to run the test with",
            options = JvmVendor.values().map { it.displayName to it.name }
        )
        text(
            testTaskOptionsParameterName,
            "",
            display = ParameterDisplay.PROMPT,
            allowEmpty = true,

    }

    interface ActualWithoutExpect : KtFirDiagnostic<KtNamedDeclaration> {
        override val diagnosticClass get() = ActualWithoutExpect::class
        val declaration: KtSymbol
        val compatibility: Map<ExpectActualCompatibility<FirBasedSymbol<*>>, List<KtSymbol>>
    }

    interface AmbiguousExpects : KtFirDiagnostic<KtNamedDeclaration> {
        override val diagnosticClass get() = AmbiguousExpects::class

                "GRADLE_RUNNER",
                "KILL_PROCESSES_STARTED_BY_GRADLE",
                "CHECK_CLEAN_M2_ANDROID_USER_HOME"
            ),
            performanceTest.steps.items.map(BuildStep::name)
        )

        val expectedRunnerParams = listOf(
            "-PperformanceBaselines=%performance.baselines%",
            "-PtestJavaVersion=17",
            "-PtestJavaVendor=openjdk",

    allValueArguments.map { (name, value) ->
        KtNamedAnnotationValue(name, value.toKtAnnotationValue(analysisContext), analysisContext.token)
    }

internal fun CallableDescriptor.createContextReceivers(
    analysisContext: Fe10AnalysisContext
): List<KtContextReceiver> {
    return contextReceiverParameters.map { createContextReceiver(it, analysisContext) }
}

        KtFe10FunctionLikeSignature(
            symbol,
            substitutor.substitute(returnType),
            receiverType?.let { substitutor.substitute(it) },
            valueParameters.map { valueParameter ->
                KtFe10VariableLikeSignature<KtValueParameterSymbol>(
                    valueParameter.symbol,
                    substitutor.substitute(valueParameter.returnType),

    *   Enable `map_and_batch_fusion` and `noop_elimination` optimizations by
        default. They can be disabled by configuring
        `tf.data.experimental.OptimizationOptions` to set `map_and_batch =
        False` or `noop_elimination = False` respectively. To disable all
        default optimizations, set `apply_default_optimizations = False`.
    *   Support parallel map in `map_and_filter_fusion`.

internal class ActualWithoutExpectImpl(
    override val declaration: KtSymbol,
    override val compatibility: Map<ExpectActualCompatibility<FirBasedSymbol<*>>, List<KtSymbol>>,
    firDiagnostic: KtPsiDiagnostic,
    token: KtLifetimeToken,

                it.getTopLevelClassIds()
            }
            .filter { it.packageFqName == packageFqName }
            .map { it.shortClassName }

        return generatedTopLevelClassifiers.toSet()
    }

    private fun collectGeneratedTopLevelCallables(packageFqName: FqName, session: FirSession): Set<Name> {