#  1) make target calls ./tools/docker
#  2) ./tools/docker calls `make build.docker.x` targets to compute the dependencies required
#  3) ./tools/docker triggers the actual docker commands required
# As a result, there are two layers of make involved.

docker: ## Build all docker images
	./tools/docker

docker.save: ## Build docker images and save to tar.gz
	./tools/docker --save

docker.push: ## Build all docker images and push to

	// fsStatsLogs identifies stats for pod logs.
	fsStatsLogs fsStatsType = "logs"
	// fsStatsRoot identifies stats for pod container writable layers.
	fsStatsRoot fsStatsType = "root"
	// fsStatsContainer identifies stats for pod container read-only layers
	fsStatsImages fsStatsType = "images"
)

// Config holds information about how eviction is configured.
type Config struct {

AR_IMAGE_PATH="us-central1-docker.pkg.dev/tensorflow-sigs/tensorflow/build-arm64"

# Build for both JAX and TF usage.  We do these in one place because they share
# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG"
  AR_IMAGE="$AR_IMAGE_PATH:$target-$TAG"
  docker pull "$IMAGE" || true

    "access_log_path": "{{.AccessLogPath}}",
    "address": {
      "socket_address": {
        "address": "0.0.0.0",
        "port_value": {{.Ports.AdminPort}}
      }
    }
  },
  "layered_runtime": {
      "layers": [
          {
              "name": "deprecation",
              "static_layer": {
                  "envoy.deprecated_features:envoy.config.listener.v3.Listener.hidden_envoy_deprecated_use_original_dst": true,

	}
	audit.LogRequestMetadata(ctx, req, requestReceivedTimestamp, rac.Level, attribs)

	return ac, nil
}

// writeLatencyToAnnotation writes the latency incurred in different
// layers of the apiserver to the annotations of the audit object.
// it should be invoked after ev.StageTimestamp has been set appropriately.
func writeLatencyToAnnotation(ctx context.Context, ev *auditinternal.Event) {

"usability.observability.status" is the feature, and "exist-by-default" is the scenario in the first case, and the second case has no scenario.

The hierarchical nature of feature labels allows us to aggregate data about related feature sets.  To provide for consistent reporting and aggregation, we have defined the top two layers of hierarchy, and ask that you place your feature under these headings.  For more detail on the purpose of each heading, see Top-Level Feature Headings below.  If you feel that...

 *   Added `jit_compile` as a settable property to `tf.keras.Model`.
 *   Added `synchronized` optional parameter to `layers.BatchNormalization`.

num_channels = 1

# Training parameters.
learning_rate = 0.001
display_step = 10
batch_size = 32

# Network parameters.
n_hidden_1 = 32  # 1st conv layer number of neurons.
n_hidden_2 = 64  # 2nd conv layer number of neurons.
n_hidden_3 = 64  # 1st fully connected layer of neurons.
flatten_size = num_features // 16 * n_hidden_2

seed = 66478


class FloatModel(tf.Module):
  """Float inference for mnist model."""

  // CHECK-SAME: %[[CONV2]]
  // CHECK-SAME: data_format = "NHWC"

  // ------------------------------------------------------------------------ //
  // Add results of convolution layers #1 and #2.
  // ------------------------------------------------------------------------ //

  %14 = "tf.AddV2"(%10, %12) : (tensor<?x256x56x56xf32>, tensor<?x256x56x56xf32>) -> tensor<?x256x56x56xf32>

  },
  "layered_runtime": {
      "layers": [
          {
            "name": "global config",