func extractDockerImage(img v1.Image) ([]byte, error) {
	layers, err := img.Layers()
	if err != nil {
		return nil, fmt.Errorf("could not fetch layers: %v", err)
	}

	// The image must have at least one layer.
	if len(layers) == 0 {
		return nil, errors.New("number of layers must be greater than zero")
	}

	layer := layers[len(layers)-1]
	mt, err := layer.MediaType()
	if err != nil {

	// If the container filesystem and image filesystem are not separate,
	// than imagefs can store both image layers and writeable layers.
	SignalImageFsAvailable Signal = "imagefs.available"
	// SignalImageFsInodesFree is amount of inodes available on filesystem that container runtime uses for storing images layers.
	// If the container filesystem and image filesystem are not separate,

// CHECK: -------- Quantization Summary --------
// CHECK: Number of quantized layers in the model
// CHECK: --------------------------------
// CHECK: Name    Count/Total
// CHECK: ================================
// CHECK: Conv2D  1/1

// CHECK: Number of quantized layers with quantized outputs: 0/1
// CHECK: Number of quantize layers added: 1
// CHECK: Number of dequantize layers added: 0
}

// -----

 *
 * In order to generate realistic project dependencies, we split the projects to layers and generate
 * dependencies to projects in lower layers. The sizes of different layers are created so that
 * the first and last layers are the smallest and the layers in the middle are the largest.
 *
 * For example when numberOfProjects = 150, 10 layers will be created with sizes of
 * [1, 2, 6, 16, 50, 50, 16, 6, 2, 1]
 *

# Some tf endpoints actually lives under Keras.
_current_module.layers = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.layers",
    name="layers",
    mode="v1")
if _tf_uses_legacy_keras:
  _module_dir = _module_util.get_parent_dir_for_name(
      "tf_keras.api._v1.keras.__internal__.legacy.layers")
else:
  _module_dir = _module_util.get_parent_dir_for_name(

// CHECK: -------- Quantization Summary --------
// CHECK: Number of quantized layers in the model
// CHECK: --------------------------------
// CHECK: Name    Count/Total
// CHECK: ================================
// CHECK: Conv2D  1/2

// CHECK: Number of quantized layers with quantized outputs: 1/1
// CHECK: Number of quantize layers added: 1
// CHECK: Number of dequantize layers added: 1
}

// -----

module {

def mnist_model():
  """Creates a MNIST model."""
  model = tf.keras.models.Sequential()
  model.add(tf.keras.layers.Flatten())
  model.add(tf.keras.layers.Dense(128, activation='relu'))
  model.add(tf.keras.layers.Dense(10, activation='softmax'))
  return model


class TestModule(tf.Module):

  def __init__(self):
    super(TestModule, self).__init__()

			t.Fatal("extractOCIArtifactImage should fail due to invalid number of layers")
		}
	})

	t.Run("invalid media types", func(t *testing.T) {
		// Create the image with invalid media types.
		layer, err := random.Layer(1000, "aaa")
		if err != nil {
			t.Fatal(err)
		}
		img, err := mutate.Append(empty.Image, mutate.Addendum{Layer: layer}, mutate.Addendum{Layer: layer})
		if err != nil {

# Some tf endpoints actually lives under Keras.
_current_module.layers = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.layers",
    name="layers",
    mode="v1")
if _tf_uses_legacy_keras:
  _module_dir = _module_util.get_parent_dir_for_name(
      "tf_keras.api._v1.keras.__internal__.legacy.layers")
else:
  _module_dir = _module_util.get_parent_dir_for_name(

Abstract classes Concrete classes Cycles Name Number of classes Best Fragment Size Girth Radius Diameter Layer Package Cycles Name Number of packagess Best Fragment Size Girth Radius Diameter Layer Layers Layer Number of classes Classes of layer : Classes and Packages Click on or to go to the cycle to which the class/package belongs. Class/Package Size Used by Uses internal Uses external Layer Source(s) # Member of cycle Classes using : uses: true uses: Packages using : uses: true uses: Classes of...