yield.getOperands().begin());
}

mlir::LogicalResult ControlNodeOp::verify() {
  ControlNodeOp control_node = *this;
  if (!control_node.GetBody().args_empty())
    return control_node.emitOpError() << "expects body without any arguments";

  Operation& yield = control_node.GetBody().back();
  if (!isa<YieldOp>(yield))
    return yield.emitOpError()

)


private
val Class<*>.relevantFields: Sequence<Field>
    get() = declaredFields.asSequence()
        .filterNot { field ->
            field.isStatic
                || field.isTransient
                || Workarounds.isIgnoredBeanField(field)
        }.filter { field ->
            isNotAbstractTaskField(field)
                || field.name in abstractTaskRelevantFields
        }

          taskRunner: TaskRunner,
          nanos: Long,
        ) {
          taskRunner.assertThreadHoldsLock()
          check(waitingCoordinatorTask == null)
          if (nanos == 0L) return

          // Yield until notified, interrupted, or the duration elapses.
          val waitUntil = nanoTime + nanos
          val self = currentTask
          waitingCoordinatorTask = self
          waitingCoordinatorNotified = false

        "tf.saved_model.save(tf_module, saved_model_dir)\n",
        "\n",
        "def calibration_dataset():\n",
        "  rng = np.random.default_rng(seed=1235)\n",
        "  for _ in range(2):\n",
        "    yield {\n",
        "        'lhs_operand': rng.uniform(low=-1.0, high=1.0, size=input_shape).astype(\n",
        "            np.float32\n",
        "        )\n",
        "    }\n",

                for (child in kotlinFile.declarations) {
                    if (child is KtScript) {
                        yieldAll(child.declarations)
                    } else {
                        yield(child)
                    }
                }
            }
        }

    override fun getClassLikeDeclarationByClassId(classId: ClassId): KtClassLikeDeclaration? {

            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>,

      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'

    %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_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 {