namespace mlir {
namespace quant {

UnrankedTensorType CreateUnknownShapeFromElementType(Type tensor_type) {
  if (!mlir::cast<TensorType>(tensor_type)) return UnrankedTensorType();
  return UnrankedTensorType::get(
      mlir::cast<TensorType>(tensor_type).getElementType());
}

}  // namespace quant

  const auto gemm_style_op_result_type =
      mlir::cast<RankedTensorType>(gemm_style_op_result.getType());
  const ArrayRef<int64_t> gemm_style_shape =
      gemm_style_op_result_type.getShape();

  Type accumulation_quantized_element_type;
  TensorType new_gemm_style_op_result_type;

  const double input_scale =
      mlir::cast<UniformQuantizedType>(getElementTypeOrSelf(input_type))
          .getScale();

		),
	}
	c.syncHandler = c.handleVersionUpdate

	crdinformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{
		AddFunc: func(obj interface{}) {
			cast := obj.(*apiextensionsv1.CustomResourceDefinition)
			c.enqueueCRD(cast)
		},
		UpdateFunc: func(oldObj, newObj interface{}) {
			// Enqueue both old and new object to make sure we remove and add appropriate API services.

        ClassImplementationSnapshot currentImplementation = Cast.uncheckedNonnullCast(thisExecution.getImplementation());
        ClassImplementationSnapshot previousImplementation = Cast.uncheckedNonnullCast(lastExecution.getImplementation());
        ImmutableList<ImplementationSnapshot> currentAdditionalImplementations = Cast.uncheckedNonnullCast(thisExecution.getAdditionalImplementations());

      // First create an identity permutation tensor.
      for (int i = 0; i < input_rank; i++) {
        permute.push_back(rewriter.getI32IntegerAttr(i));
      }
      // Swaps the last two dimension since the last two dimension will be
      // mapped to X and Z dimension.
      std::iter_swap(permute.begin() + input_rank - 1,
                     permute.begin() + input_rank - 2);

		FilterFunc: func(obj interface{}) bool {
			if cast, ok := obj.(*corev1.ConfigMap); ok {
				return cast.Name == c.configmapName && cast.Namespace == c.configmapNamespace
			}
			if tombstone, ok := obj.(cache.DeletedFinalStateUnknown); ok {
				if cast, ok := tombstone.Obj.(*corev1.ConfigMap); ok {
					return cast.Name == c.configmapName && cast.Namespace == c.configmapNamespace
				}
			}

		FilterFunc: func(obj interface{}) bool {
			if cast, ok := obj.(*corev1.ConfigMap); ok {
				return cast.Name == c.configmapName && cast.Namespace == c.configmapNamespace
			}
			if tombstone, ok := obj.(cache.DeletedFinalStateUnknown); ok {
				if cast, ok := tombstone.Obj.(*corev1.ConfigMap); ok {
					return cast.Name == c.configmapName && cast.Namespace == c.configmapNamespace
				}
			}

        return type;
    }

    @Override
    public void setFromAnyValue(Object object) {
        if (object instanceof Provider) {
            set(Cast.<Provider<T>>uncheckedNonnullCast(object));
        } else {
            set(Cast.<T>uncheckedNonnullCast(object));
        }
    }

    @Override
    public void set(@Nullable T value) {
        if (value == null) {
            discardValue();

     * smart-casts:
     *
     * ```kt
     * if (this is String) {
     *   this.substring() // 'this' receiver is explicit, so no implicit smart-cast here.
     *
     *   smartcast() // 'this' receiver is implicit, therefore there is implicit smart-cast involved.
     * }
     * ```
     */
    public fun KtExpression.getImplicitReceiverSmartCast(): Collection<KaImplicitReceiverSmartCast> =

            (getElementTypeOrSelf(op.getType())))) {
      return failure();
    }

    // Remove identity reshape with both static result and input shape.
    auto result_type = mlir::cast<ShapedType>(op.getType());
    auto input_type = mlir::cast<ShapedType>(op.getInput().getType());

    // Constant folding
    // If the result type isn't static, tries to derive the result type from
    // the #2 operand.