"lds_config": {
      "resource_api_version": "V3",
      "ads": {}
    },
    "cds_config": {
      "resource_api_version": "V3",
      "ads": {}
    },
    "ads_config": {
      "api_type": "GRPC",
      "transport_api_version": "V3",
      "grpc_services": [
        {
          "google_grpc": {
            "target_uri": "{{ .discovery_address }}",
            "stat_prefix": "googlegrpcxds",

fi

git clone  "https://${REPO}" "${API_TMP}" && cd "${API_TMP}"
git checkout "${SHA}"
if [ ! -f "${API_TMP}/kubernetes/customresourcedefinitions.gen.yaml" ]; then
  echo "Generated Custom Resource Definitions file does not exist in the commit SHA ${SHA}. Not updating the CRD file."
  exit
fi
rm -f "${ROOTDIR}/manifests/charts/base/crds/crd-all.gen.yaml"

#include "tensorflow/c/experimental/ops/gen/model/arg_type.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace generator {
namespace cpp {

ArgTypeView::ArgTypeView(ArgType arg_type) : arg_type_(arg_type) {}

string ArgTypeView::TypeName() const {
  if (arg_type_.is_read_only()) {

      if (current_type == input_type) {  // int8 == int8
        arg_type = quantize_output.getType();
        new_arg = block.addArgument(arg_type, loc);
        quantize_output.replaceAllUsesWith(new_arg);
      } else if (input_type.isUnsignedInteger(
                     current_type.getIntOrFloatBitWidth())) {  // int8 != uint8
        arg_type = quant::ConvertSignedQuantizedToUnsigned(
            quantize_output.getType(), loc);

          func, arg_number, symbol_table);
      if (!global_tensor) continue;

      auto arg_type = mlir::cast<RankedTensorType>(arg.getType());
      assert(arg_type.getRank() == 0);
      llvm::ArrayRef<TensorType> underlying_type =
          mlir::cast<TF::ResourceType>(arg_type.getElementType()).getSubtypes();

      // If the arg type already matches the global_tensor type, we don't need

  if (IsList()) {
    return "AddInputList";
  } else {
    return "AddInput";
  }
}

std::vector<string> ArgView::SetterArgs() const { return {VariableName()}; }

bool ArgView::IsList() const { return arg_.arg_type().is_list(); }

int ArgView::Position() const { return arg_.position(); }

}  // namespace cpp
}  // namespace generator

OpArgumentView::OpArgumentView(string type, string var, string def)
    : type_name_(type), variable_name_(var), default_value_(def) {}

OpArgumentView::OpArgumentView(ArgSpec arg)
    : type_name_(ArgTypeView(arg.arg_type()).TypeName()),
      variable_name_(ArgView(arg).VariableName()) {}

OpArgumentView::OpArgumentView(AttrSpec attr)
    : type_name_(AttrView(attr).VariableType()),
      variable_name_(AttrView(attr).VariableName()),

  LogicalResult matchAndRewrite(stablehlo::PadOp pad_op,
                                PatternRewriter& rewriter) const override {
    Value pad_input = pad_op.getOperand();
    RankedTensorType pad_type = pad_op.getType().cast<RankedTensorType>();

    auto transpose_op = pad_input.getDefiningOp<stablehlo::TransposeOp>();
    if (!transpose_op || !transpose_op->hasOneUse()) return failure();

                           TF_Status* s) {
  unwrap(o)->outputs.push_back(unwrap(tensor));
}

void TF_AbstractOpSetOpType(TF_AbstractOp* op, const char* const op_type,
                            TF_Status* s) {
  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Reset(op_type,
                           /*raw_device_name=*/nullptr));
}

void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,