// those as well. The minimum processor requirement is POWER8 (ISA 2.07).
// The struct is padded to avoid false sharing.
var PPC64 struct {
	_         CacheLinePad
	HasDARN   bool // Hardware random number generator (requires kernel enablement)
	HasSCV    bool // Syscall vectored (requires kernel enablement)
	IsPOWER8  bool // ISA v2.07 (POWER8)
	IsPOWER9  bool // ISA v3.00 (POWER9)
	IsPOWER10 bool // ISA v3.1  (POWER10)

	return traceArg(uint64(base)-trace.minPageHeapAddr) / fixedStack
}

// traceCompressStackSize assumes size is a power of 2 and returns log2(size).
func traceCompressStackSize(size uintptr) traceArg {
	if size&(size-1) != 0 {
		throw("goroutine stack size is not a power of 2")
	}
	return traceArg(sys.Len64(uint64(size)))

        <option value="$PROJECT_DIR$/plugins/kotlinx-serialization/testData" />
        <option value="$PROJECT_DIR$/plugins/assign-plugin/testData" />
        <option value="$PROJECT_DIR$/plugins/power-assert/testData" />
        <option value="$PROJECT_DIR$/analysis/analysis-api/testData" />
        <option value="$PROJECT_DIR$/analysis/symbol-light-classes/testData" />

// To keep the division operation efficient, we round that up to 4*64, or 256.
// Given what cputicks represents, we use this on all other platforms except
// for PowerPC.
// The suggested increment frequency for PowerPC's time base register is
// 512 MHz according to Power ISA v2.07 section 6.2, so we use 32 on ppc64
// and ppc64le.
const traceTimeDiv = (1-osHasLowResClockInt)*64 + osHasLowResClockInt*(256-224*(goarch.IsPpc64|goarch.IsPpc64le))

    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}

std::unique_ptr<OperationPass<ModuleOp>> CreateUnfoldSplatConstantPass();

// Create a pass that legalizes MHLO to TFLite dialect.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeHloToTfLitePass();

// Creates a pass that lowers stablehlo composite ops to tflite ops.
std::unique_ptr<OperationPass<ModuleOp>> CreateCompositeLoweringPass();

// Adds the HLO to TF rewrite patterns to the specified pattern list.

Ele também foi feito como referência futura.

Então você poderá voltar e ver exatamente o que precisar.

## Rode o código

Todos os blocos de código podem ser copiados e utilizados diretamente (eles são, na verdade, arquivos Python testados).

    {"/tensorflow/core/tf2xla/api/v2/phase2_compilation_time",
     "The wall-clock time spent on executing graphs in milliseconds.",
     "configuration"},
    // Power of 1.5 with bucket count 45 (> 23 hours)
    {tsl::monitoring::Buckets::Exponential(1, 1.5, 45)});

// Name of component for error logging. This name is fixed and required to
// enable logging.

namespace mlir {

namespace func {
class FuncOp;
}
class ModuleOp;
class Operation;
template <typename T>
class OperationPass;
class Pass;

namespace mhlo {

/// Lowers from TF dialect to HLO dialect. When allow_partial_conversion is
/// false, emits an error if there is any operation that can't be legalized.
/// When `tf2xla_fallback_device_type` is not `None`, also uses legalization

  let constructor = "mlir::odml::CreateUnfoldSplatConstantPass()";
}

def CompositeLoweringPass : Pass<"composite-lowering", "ModuleOp"> {
  let summary = "Lowers mhlo composites directly to tflite ops (when possible).";
  let dependentDialects = ["mlir::mhlo::MhloDialect", "TFL::TensorFlowLiteDialect"];
  let constructor = "mlir::odml::CreateCompositeLoweringPass()";
}