return success();
}

// Pattern matches the following reduction function for ArgMax/ArgMin:
// %0 = compare{GT}(%lhs_value, %rhs_value)
// %1 = compare{NE}(%lhs_value, %lhs_value)
// %2 = or(%0, %1)
// %3 = select(%2, %lhs_value, %rhs_value)
// %4 = compare{EQ}(%lhs_value, %rhs_value)
// %5 = compare{LT}(%lhs_index, %rhs_index)
// %6 = and(%4, %5)
// %7 = or(%2, %6)
// %8 = select(%7, %lhs_index, %rhs_index)

    async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]):
        return {"fresh_value": fresh_value}
    ```

=== "Python 3.8+ nicht annotiert"

    !!! tip "Tipp"
        Bevorzugen Sie die `Annotated`-Version, falls möglich.

    ```Python hl_lines="1"
    async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):

    ```Python hl_lines="1"
    async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]):
        return {"fresh_value": fresh_value}
    ```

=== "Python 3.8+ non-Annotated"

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="1"
    async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):

        dot_dimension_nums.getRhsContractingDimensions();
    const auto lhs_contracting_dims =
        dot_dimension_nums.getLhsContractingDimensions();

    const Value rhs_value = op.getRhs();
    const Value lhs_value = op.getLhs();

    Operation* rhs_op = rhs_value.getDefiningOp();
    auto filter_constant_op = dyn_cast_or_null<stablehlo::ConstantOp>(rhs_op);

    // Set to `nullptr` because this attribute only matters when the input is

  EXPECT_TRUE(statistics.has_value());
  EXPECT_EQ(statistics.value().min_max_statistics().global_min(), -5.0f);
  EXPECT_EQ(statistics.value().min_max_statistics().global_max(), 10.0f);

  collector.ClearData();
  statistics = collector.GetStatistics();
  EXPECT_FALSE(statistics.has_value());

  collector.Collect(
      /*min=*/1.0f, /*max=*/10.f, /*histogram=*/{});

DenseElementsAttr GetScalarOfType(Type ty, T raw_value) {
  RankedTensorType scalar_ty = RankedTensorType::get({}, ty);
  if (auto float_ty = mlir::dyn_cast<FloatType>(ty)) {
    FloatAttr attr = FloatAttr::get(float_ty, raw_value);
    return DenseElementsAttr::get(scalar_ty, attr);
  } else if (auto int_ty = mlir::dyn_cast<IntegerType>(ty)) {
    IntegerAttr attr = IntegerAttr::get(int_ty, raw_value);
    return DenseElementsAttr::get(scalar_ty, attr);

  auto op_func_attr_array = op.getOpFuncAttrs().getValue();
  for (auto op_attr : op_func_attr_array) {
    auto key_value = mlir::dyn_cast<mlir::ArrayAttr>(op_attr);
    if (!key_value || key_value.getValue().size() != 2 ||
        !mlir::isa<mlir::StringAttr>(key_value.getValue()[0]) ||
        !mlir::isa<mlir::StringAttr>(key_value.getValue()[1]))
      return op.emitOpError() << "each op_func_attr should be a key-value "

			namespaceTolerations:      []api.Toleration{},
			podTolerations:            []api.Toleration{{Key: "testKey", Operator: "Equal", Value: "testValue", Effect: "NoSchedule", TolerationSeconds: nil}},
			mergedTolerations:         []api.Toleration{{Key: "testKey", Operator: "Equal", Value: "testValue", Effect: "NoSchedule", TolerationSeconds: nil}},

  assert(op_attrs);
  op_attrs->clear();

  mlir::Builder builder(context);
  for (auto iter : op_attr_array) {
    auto key_value = mlir::cast<mlir::ArrayAttr>(iter).getValue();
    llvm::StringRef key = mlir::cast<mlir::StringAttr>(key_value[0]).getValue();
    mlir::Attribute value = key_value[1];
    op_attrs->push_back({key, value});
  }
}

mlir::ParseResult ParseExecuteOpCommon(mlir::OpAsmParser &parser,

        File oldFile = new File(args[0])
        File newFile = new File(args[1])
        PerformanceTestScenarioDefinition oldJson = new ObjectMapper().readValue(oldFile, PerformanceTestScenarioDefinition).sort()
        PerformanceTestScenarioDefinition newJson = new ObjectMapper().readValue(newFile, PerformanceTestScenarioDefinition).sort()
        if (oldJson != newJson) {