Sie können diesen Teil wahrscheinlich überspringen.

Es gibt eine alternative Möglichkeit, diese Logik zu definieren, sodass sie beim *Hochfahren* und beim *Herunterfahren* ausgeführt wird.

Aber jetzt, da FastAPI 0.99.0 und höher, OpenAPI 3.1.0 verwendet, das JSON Schema 2020-12 verwendet, und Swagger UI 5.0.0 und höher, ist alles konsistenter und die Beispiele sind in JSON Schema enthalten.

### Swagger-Benutzeroberfläche und OpenAPI-spezifische `examples`.

	callee := cs.Callee
	csflags := cs.Flags
	call := cs.Call

	// Start with the size-based score for the callee.
	score := int(callee.Inl.Cost)
	var tmask scoreAdjustTyp

	if debugTrace&debugTraceScoring != 0 {
		fmt.Fprintf(os.Stderr, "=-= scoring call to %s at %s , initial=%d\n",
			callee.Sym().Name, fmtFullPos(call.Pos()), score)
	}

  };
  if (!emplace_res.second) {
    // This callee was handled before.
    if (!info.signature_change) return success();
    return recreate_caller();
  }
  llvm::SmallDenseMap<Value, Value> callee_map;
  func::FuncOp lowered_callee = callee;
  if (!callee.isPrivate()) {
    // Clone non-private callee in case of signature change.
    lowered_callee = callee.clone();
    lowered_callee.setPrivate();
  }

  }

  // Clone the callee before making changes.
  SmallString<64> name_base = callee.getName();
  auto module = callee->getParentOfType<ModuleOp>();
  name_base += "_resource_lifted";
  auto name = name_base;
  callee = callee.clone();
  callee.setPrivate();
  callee.setName(mlir::StringAttr::get(callee->getContext(), name));
  SymbolTable(module).insert(callee);
  result->lifted_callee = callee;

    auto sym = mlir::dyn_cast<SymbolRefAttr>(attr.getValue());
    if (!sym) continue;
    auto callee = symtab.lookup<func::FuncOp>(sym.getRootReference());
    if (!callee) {
      // This is not expected to happen in practice.
      return op->emitError()
             << "Cannot find function " << sym.getRootReference();
    }
    callees.push_back(callee);
  }
  return success();
}

bool HasSingleBlock(func::FuncOp func) {

		// interfaces and implementations are mutually exclusive,
		// devirtualization can still select the correct callee for
		// each.
		//
		// If they were not mutually exclusive (for example, two Add
		// calls), then we could not definitively select the correct
		// callee.
		val += m.Multiply(42, selectA(i).Add(one(i), 2))
	}
	return val
}

type AddFunc func(int, int) int

      }
      llvm::SmallVector<func::FuncOp> callees;
      if (GetCallees(op, symtab, callees).failed()) {
        return WalkResult::interrupt();
      }
      for (auto callee : callees) {
        worklist.push(callee);
      }
      return WalkResult::advance();
    });
    if (result.wasInterrupted()) return failure();
  }
  return success();
}

  // CHECK-SAME: callee = @main_stream_0
  %handle_0 = mlrt.async(%input0, %promise_b)
    {callee = @main_stream_0} :
    (tensor<i32>, !mlrt.promise) -> !mlrt.async_handle
  // CHECK: [[handle_1:%.*]] = mlrt.async([[input1]], [[promises]]#1, [[promises]]#2)
  // CHECK-SAME: callee = @main_stream_1
  %handle_1 = mlrt.async(%input1, %promise_c, %promise_d)
    {callee = @main_stream_1} :

		if !ok {
			break
		}
		if strings.ContainsAny(caller, " \r\n") {
			t.Skip("caller contains space or newline")
		}

		callee, ok := consumeString()
		if !ok {
			break
		}
		if strings.ContainsAny(callee, " \r\n") {
			t.Skip("callee contains space or newline")
		}

		line, ok := consumeInt64()
		if !ok {
			break
		}
		weight, ok := consumeInt64()
		if !ok {