StdOutput     Handle
	StdErr        Handle
}

type StartupInfoEx struct {
	StartupInfo
	ProcThreadAttributeList *ProcThreadAttributeList
}

// ProcThreadAttributeList is a placeholder type to represent a PROC_THREAD_ATTRIBUTE_LIST.
//
// To create a *ProcThreadAttributeList, use NewProcThreadAttributeList, update
// it with ProcThreadAttributeListContainer.Update, free its memory using

     * consist strictly of loading values, though during refresh a value may be both active and
     * loading.
     */
    boolean isActive();
  }

  /** Placeholder. Indicates that the value hasn't been set yet. */
  static final ValueReference<Object, Object> UNSET =
      new ValueReference<Object, Object>() {
        @CheckForNull
        @Override
        public Object get() {

     * consist strictly of loading values, though during refresh a value may be both active and
     * loading.
     */
    boolean isActive();
  }

  /** Placeholder. Indicates that the value hasn't been set yet. */
  static final ValueReference<Object, Object> UNSET =
      new ValueReference<Object, Object>() {
        @CheckForNull
        @Override
        public Object get() {

//===--------------------------------------------------------------------===//
//  Test TF placeholder attribute
//===--------------------------------------------------------------------===//

// CHECK-LABEL: func @placeholderattr
func.func @placeholderattr() -> ()
// CHECK:    attributes {some_placeholder = #tf_type.placeholder<"foo">} {
    attributes {some_placeholder = #tf_type.placeholder<"foo">} {
  func.return
}

			return volumetypes.NewOperationContext(eventErr, detailedErr, migrated)
		}
		if resizeDone {
			return volumetypes.NewOperationContext(nil, nil, migrated)
		}
		// This is a placeholder error - we should NEVER reach here.
		err = fmt.Errorf("volume resizing failed for unknown reason")
		eventErr, detailedErr = volumeToMount.GenerateError("NodeExpandVolume.NodeExpandVolume failed to resize volume", err)

cancel-button-expected.checksum","kids":[],"cl_weight":0.003061301158710795,"touches":2,"min_t":1228781586,"max_t":1236205616,"mean_t":1232493601},{"name":"placeholder-pseudo-style-expected.checksum","kids":[],"cl_weight":0.0005553709994722575,"touches":2,"min_t":1229478785,"max_t":1236205616,"mean_t":1232842200},{"name":"password-placeholder-expected.checksum","kids":[],"cl_weight":0.0005553709994722575,"touches":2,"min_t":1229478785,"max_t":1236205616,"mean_t":1232842200},{"name":"select-chang...

Lkotlinx/coroutines/internal/LockFreeLinkedListNode$toString$1;
Lkotlinx/coroutines/internal/LockFreeLinkedListNode;
Lkotlinx/coroutines/internal/LockFreeTaskQueue;
Lkotlinx/coroutines/internal/LockFreeTaskQueueCore$Placeholder;
Lkotlinx/coroutines/internal/LockFreeTaskQueueCore;
Lkotlinx/coroutines/internal/MainDispatcherFactory;
Lkotlinx/coroutines/internal/MainDispatcherLoader$$ExternalSyntheticServiceLoad0;

            "be string attributes");
        return std::nullopt;
      }
    }
  }
  // Runtime version string is generated after we update the op
  // versions. Here we put a 16-byte dummy string as a placeholder. We choose
  // 16-byte because it's the alignment of buffers in flatbuffer, so it won't
  // cause any waste of space if the actual string is shorter than 16 bytes.
  constexpr std::size_t kByteStringSize = 16;

}

func TestQueryNamedArg(t *testing.T) {
	db := newTestDB(t, "people")
	defer closeDB(t, db)
	prepares0 := numPrepares(t, db)
	rows, err := db.Query(
		// Ensure the name and age parameters only match on placeholder name, not position.
		"SELECT|people|age,name|name=?name,age=?age",
		Named("age", 2),
		Named("name", "Bob"),
	)
	if err != nil {
		t.Fatalf("Query: %v", err)
	}
	type row struct {
		age  int

    )
    self.assertDictEqual(original_signature_map, converted_signature_map)

  def test_duplicated_tensor_name(self):
    with session.Session(graph=ops.Graph()) as sess:
      input_tensor = array_ops.placeholder(
          dtypes.float32, shape=[], name='input'
      )
      q_input = array_ops.fake_quant_with_min_max_args(
          input_tensor, min=-0.1, max=0.2, num_bits=8, narrow_range=False
      )