is SystemProperty -> trace
            is Project -> trace
            else -> null
        }
}


enum class PropertyKind {
    Field {
        override fun toString() = "field"
    },
    PropertyUsage {
        override fun toString() = "property usage"
    },
    InputProperty {
        override fun toString() = "input property"
    },
    OutputProperty {

        return scope.getClassifierNames() ?: emptySet()
    }

    override fun getConstructors(): Sequence<KaConstructorSymbol> = sequence {
        constructors.forEach { yield(it.toKtConstructorSymbol(analysisContext)) }
    }
}

internal open class KaFe10ScopeNonStaticMember(
    scope: MemberScope,
    constructors: Collection<ConstructorDescriptor>,

* می تواند کاری با **پاسخ** انجام دهید یا هر کد مورد نیازتان را اجرا کند.
* سپس **پاسخ** را برمی گرداند.

!!! توجه "جزئیات فنی"
    در صورت وجود وابستگی هایی با `yield`، کد خروجی **پس از** اجرای میان‌‌افزار اجرا خواهد شد.

    در صورت وجود هر گونه وظایف پس زمینه (که در ادامه توضیح داده می‌شوند)، تمام میان‌افزارها *پس از آن* اجرا خواهند شد.

## ساخت یک میان افزار

      outlier = np.random.uniform(low=0, high=10, size=(1, 3, 4, 3)).astype(
          'f4'
      )
      outlier[0][0][0][0:2] = [-1000, 1000]
      yield {'input_tensor': ops.convert_to_tensor(outlier)}
      for _ in range(10):
        yield {
            'input_tensor': ops.convert_to_tensor(
                np.random.uniform(low=0, high=10, size=(1, 3, 4, 3)).astype(
                    'f4'

private
val Project.hierarchy: Sequence<Project>
    get() = sequence {
        var project = this@hierarchy
        yield(project)
        while (project != project.rootProject) {
            project = project.parent!!
            yield(project)
        }
    }


private
val Project.isLocationAwareEditorHintsEnabled: Boolean

    %4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

      "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
    ^bb0(%arg2: tensor<i32>, %arg3: tensor<128x1024xf32>):
      %1 = "tf.XlaSharding"(%arg3) <{_XlaSharding = "\0A\0B\0C", sharding = "\0A\0B\0C"}> {unspecified_dims = []} : (tensor<128x1024xf32>) -> tensor<128x1024xf32>
      %2 = "tf.Square"(%1) : (tensor<128x1024xf32>) -> tensor<128x1024xf32>
      "tf.Yield"(%arg2, %2) : (tensor<i32>, tensor<128x1024xf32>) -> ()

          tf_device.return %c, %b : tensor<f32>, tensor<i32>
        }) {device = "CPU:0"} : () -> (tensor<f32>, tensor<i32>)
        %d = "tf.opD"() : () -> tensor<i1>
        tf_executor.yield %a, %launch#0, %launch#1, %d :
                          tensor<i1>, tensor<f32>, tensor<i32>, tensor<i1>
      }
    ```

    Will be transformed into:

    ```mlir
      %island:5 = tf_executor.island {

        """
        return await super().close()

    @classmethod
    def __get_validators__(cls: Type["UploadFile"]) -> Iterable[Callable[..., Any]]:
        yield cls.validate

    @classmethod
    def validate(cls: Type["UploadFile"], v: Any) -> Any:
        if not isinstance(v, StarletteUploadFile):
            raise ValueError(f"Expected UploadFile, received: {type(v)}")

  std::unique_ptr<Variable> var;
  ASSERT_NE(absl::OkStatus(),
            internal::LoadSavedVariable(context(), saved_variable, &var));
}

// Assigning and reading values should yield
// consistent results.
TEST_P(SavedVariableLoadingTest, AssignAndReadVariableSuccesful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);