// "third_party/tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc" is
// resolved
LogicalResult PrecheckForXlaConvV2Op(XlaConvV2Op op) {
  auto input_tensor = op.getLhs();
  auto kernel_tensor = op.getRhs();
  auto window_strides = op.getWindowStrides();
  auto padding = op.getPadding();
  auto lhs_dilation = op.getLhsDilation();
  auto rhs_dilation = op.getRhsDilation();

    if (!dot.getLhs().getType().hasStaticShape() ||
        !dot.getRhs().getType().hasStaticShape())
      return rewriter.notifyMatchFailure(
          dot, "all dot_general operands must be statically shaped");
    if (dot.getLhs().getType().getElementType() !=
            first_dot.getLhs().getType().getElementType() ||
        dot.getRhs().getType().getElementType() !=

			mockPodsProvider.EXPECT().GetPods().Return(tc.pods).AnyTimes()
			mockDevicesProvider.EXPECT().GetDevices(string(podUID), containerName).Return(tc.devices).AnyTimes()
			mockCPUsProvider.EXPECT().GetCPUs(string(podUID), containerName).Return(tc.cpus).AnyTimes()
			mockMemoryProvider.EXPECT().GetMemory(string(podUID), containerName).Return(tc.memory).AnyTimes()

                    // create and wire the custom dependencies extension's dependencies to these global configurations
                    api.fromDependencyCollector(getRestricted().getDependencies().getApi());
                    implementation.fromDependencyCollector(getRestricted().getDependencies().getImplementation());
                }
            }
        """

func CheckAutoScaleAndReplicaCount(values *valuesv1alpha1.Values, spec *v1alpha1.IstioOperatorSpec) (errs util.Errors, warnings []string) {
	if values.GetPilot().GetAutoscaleEnabled().GetValue() && spec.GetComponents().GetPilot().GetK8S().GetReplicaCount() > 1 {
		warnings = append(warnings,
			"components.pilot.k8s.replicaCount should not be set when values.pilot.autoscaleEnabled is true")
	}

  return ConvertDot(
      rewriter, dot_op.getLhs(), dot_op.getRhs(), dot_dimension_numbers,
      mlir::cast<ShapedType>(dot_op.getResult().getType()), dot_op.getLoc());
}

Value ConvertDotGeneralOp(PatternRewriter& rewriter, Operation* old_op) {
  auto dot_general_op = cast<mhlo::DotGeneralOp>(old_op);
  return ConvertDot(
      rewriter, dot_general_op.getLhs(), dot_general_op.getRhs(),
      dot_general_op.getDotDimensionNumbers(),

lint: getdeps ## runs golangci-lint suite of linters
	@echo "Running $@ check"
	@$(GOLANGCI) run --build-tags kqueue --timeout=10m --config ./.golangci.yml

lint-fix: getdeps ## runs golangci-lint suite of linters with automatic fixes
	@echo "Running $@ check"

    if (!mlir::cast<ShapedType>(conv_op.getRhs().getType()).hasStaticShape()) {
      return false;
    }
    if (!mlir::cast<ShapedType>(conv_op.getLhs().getType()).hasStaticShape() &&
        !mlir::cast<ShapedType>(conv_op.getType()).hasStaticShape()) {
      auto dnums = conv_op.getDimensionNumbers();
      auto lhs_type = mlir::cast<ShapedType>(conv_op.getLhs().getType());

}

template <typename Op, typename ElementType = Type, typename ValType,
          typename Convert>
static Attribute BinaryFolder(Op *op) {
  auto lhs_op = op->getLhs().template getDefiningOp<mhlo::ConstantOp>();
  auto rhs_op = op->getRhs().template getDefiningOp<mhlo::ConstantOp>();
  if (!lhs_op || !lhs_op) return {};

  auto lhs = dyn_cast_or_null<DenseElementsAttr>(lhs_op.getValue());

    Value constant_val = add_op ? add_op.getRhs() : sub_op.getRhs();
    if (!matchPattern(constant_val, m_Constant(&added_value))) return failure();

    // Check the add op is applied to begin.
    mlir::TypedValue<::mlir::TensorType> begin_tensor =
        strided_slice_op.getBegin();
    mlir::TypedValue<::mlir::TensorType> add_source_tensor =
        add_op ? add_op.getLhs() : sub_op.getLhs();
    if (begin_tensor != add_source_tensor) {