// from DEBUGGER_TYPE_FLOAT_PER_LAYER). Each layer in the debugging model
    // has a DumpTensor, and it is used to save the entire value of outputs from
    // both the quantized and unquantized layer.
    DEBUGGER_TYPE_INT_PER_LAYER = 2;
    // DEBUGGER_TYPE_FLOAT_PER_LAYER creates a debugging model with both
    // quantized and unquantized layers. The unquantized layer's input come from

namespace mlir {
namespace quant {
namespace {

using QuantizationUnit =
    tensorflow::quantization::UnitWiseQuantizationSpec::QuantizationUnit;

// Adds QuantizationUnitLoc to quantizable layers.
class AddQuantizationUnitLocPass
    : public PassWrapper<AddQuantizationUnitLocPass,
                         OperationPass<func::FuncOp>> {
 public:

{
  "node": {
    {{ if (index .metadata.Labels "td.networking.gke.io/mesh-name") }}
      "id": "projects/{{ .gcp_project_number }}/networks/mesh:{{ (index .metadata.Labels "td.networking.gke.io/mesh-name") }}/nodes/{{ .nodeID}}",
    {{ else if (index .metadata.Labels "td.networking.gke.io/gateway-name") }}
      "id": "projects/{{ .gcp_project_number }}/networks/scope:{{ (index .metadata.Labels "td.networking.gke.io/gateway-name") }}/nodes/{{ .nodeID}}",
    {{ else }}

	// 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

	}
	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`.

  // 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",