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
}

// -----

# 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 {

			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(

	if err != nil {
		return "", fmt.Errorf("failed to get manifest: %v", err)
	}
	if len(manifest.Layers) != 1 {
		return "", fmt.Errorf("docker image does not have one layer (got %v)", len(manifest.Layers))
	}

	return fmt.Sprintf("https://%v/v2/%v/blobs/%v", *registry, imageName, manifest.Layers[0].Digest.String()), nil
}

func (h *Handler) ServeHTTP(w http.ResponseWriter, r *http.Request) {

	// it should return an error and allow higher layers to
	// enumerate the directory.
	GetConsumption(path string, id QuotaID) (int64, error)

	// GetInodes returns the number of inodes used by the
	// directory, determined by the implementation's quota-based
	// mechanism.  If it is unable to do so using that mechanism,
	// it should return an error and allow higher layers to
	// enumerate the directory.

# -----------------------------------------------------------------------------
# Specification
# -----------------------------------------------------------------------------

# Card size and layers
size: { width: 1200, height: 630 }
layers:

  # Background
  - background:
      image: *background_image
      color: *background_color

  # Logo
  - size: { width: 144, height: 144 }
    offset: { x: 992, y: 64 }