TensorHandlePtr first_combined_value = CreatePerDeviceValues(
      context.get(), components, first_device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Nest the first parallel tensor into a second
  TensorHandlePtr value_three(FloatTensorHandle(3., status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  components[0] = first_combined_value.get();

	if !ok {
		id = len(b.strings)
		b.strings = append(b.strings, s)
		b.stringMap[s] = id
	}
	return int64(id)
}

func (b *profileBuilder) flush() {
	const dataFlush = 4096
	if b.pb.nest == 0 && len(b.pb.data) > dataFlush {
		b.zw.Write(b.pb.data)
		b.pb.data = b.pb.data[:0]
	}
}

// pbValueType encodes a ValueType message to b.pb.

// summary represent the largest section of address space (16 GiB on 64-bit systems),
// with each subsequent level representing successively smaller subsections until we
// reach the finest granularity at the leaves, a chunk.
//
// More specifically, each summary in each level (except for leaf summaries)
// represents some number of entries in the following level. For example, each

    Daher ist es wichtig, dies **nahe dem Ende** des `Dockerfile`s zu platzieren, um die Erstellungszeiten des Containerimages zu optimieren.

6. Lege den **Befehl** fest, um den `uvicorn`-Server zu starten.

    `CMD` nimmt eine Liste von Zeichenfolgen entgegen. Jede dieser Zeichenfolgen entspricht dem, was Sie durch Leerzeichen getrennt in die Befehlszeile eingeben würden.

// successfully unwound the entire stack.
func (u *unwinder) finishInternal() {
	u.frame.pc = 0

	// Note that panic != nil is okay here: there can be leftover panics,
	// because the defers on the panic stack do not nest in frame order as
	// they do on the defer stack. If you have:
	//
	//	frame 1 defers d1
	//	frame 2 defers d2
	//	frame 3 defers d3
	//	frame 4 panics
	//	frame 4's panic starts running defers

  return %outputs_26 : tensor<?x224x224x3xui8>
}

// -----
// Test a nest while in which the while body is after the usage.

// CHECK-LABEL: nested_while
func.func @nested_while(%arg0: tensor<?xf32> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}) -> (tensor<16x16x?xf32>)  {

// linearized function argument or return on a FunctionDef, and hence to an
// mlir::func::FuncOp argument / return.
//
// This must match the linearization that happens in `tf.nest.flatten`.
// In particular, dict values should be linearized in sorted key order.
//
// The linearized index paths can be returned back to a structured

	}
	// match: (MOVBUreg x:(FEQS _ _))
	// result: x
	for {
		x := v_0
		if x.Op != OpRISCV64FEQS {
			break
		}
		v.copyOf(x)
		return true
	}
	// match: (MOVBUreg x:(FNES _ _))
	// result: x
	for {
		x := v_0
		if x.Op != OpRISCV64FNES {
			break
		}
		v.copyOf(x)
		return true
	}
	// match: (MOVBUreg x:(FLED _ _))
	// result: x
	for {

*   `tf.experimental.ExtensionType`

    *   `tf.experimental.ExtensionType` now supports Python `tuple` as the type annotation of its fields.

*   `tf.nest`

    *   Deprecated API `tf.nest.is_sequence` has now been deleted. Please use `tf.nest.is_nested` instead.

## Keras

Keras is a framework built on top of the TensorFlow. See more details on the [Keras website](https://keras.io/).

### Breaking Changes

	type1 := TypeOf(t1)
	if field, ok = type1.FieldByName("int"); !ok {
		t.Fatal("no field 'int'")
	}
	if field.Index[0] != 1 {
		t.Error("field index should be 1; is", field.Index)
	}
}

type FTest struct {
	s     any
	name  string
	index []int
	value int
}

type D1 struct {
	d int
}
type D2 struct {
	d int
}

type S0 struct {
	A, B, C int
	D1