// 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(

# 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(

    // 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 }}

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

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

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

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