Destination: &networking.Destination{Host: "foo.baz.east"},
				Weight:      75,
			}},
		}, valid: true},
		{name: "total weight > 100", route: &networking.HTTPRoute{
			Route: []*networking.HTTPRouteDestination{{
				Destination: &networking.Destination{Host: "foo.baz.south"},
				Weight:      550,
			}, {
				Destination: &networking.Destination{Host: "foo.baz.east"},
				Weight:      500,
			}},

	c.Assert(err, nil)
	// verify the status of the HTTP response.
	c.Assert(response.StatusCode, http.StatusOK)

	// retrieve the info of last modification time of the object from the response header.
	lastModified := response.Header.Get("Last-Modified")
	// Parse it into time.Time structure.
	t, err := time.Parse(http.TimeFormat, lastModified)
	c.Assert(err, nil)

	Copy(copy func(AST) AST, skip func(AST) bool) AST

	// Implement the fmt.GoStringer interface.
	GoString() string
	goString(indent int, field string) string
}

// ASTToString returns the demangled name of the AST.
func ASTToString(a AST, options ...Option) string {
	tparams := true
	enclosingParams := true
	llvmStyle := false
	max := 0
	for _, o := range options {
		switch {
		case o == NoTemplateParams:

    Operation* filter_op = filter_value.getDefiningOp();
    auto filter_uniform_quantized_type =
        GetElementType(filter_value).cast<UniformQuantizedPerAxisType>();
    auto filter_constant_value_attr = cast<DenseIntElementsAttr>(
        cast<stablehlo::ConstantOp>(filter_value.getDefiningOp()).getValue());
    const DenseIntElementsAttr new_filter_value_attr =

    return false;
  }
  if (mlir::cast<ShapedType>(sq_op.getType()).getRank() - 1 ==
          *axis.getValues<int>().begin() ||
      *axis.getValues<int>().begin() == -1) {
    return true;
  }
  if (mlir::cast<ShapedType>(sq_op.getType()).getRank() !=
      axis.getNumElements()) {
    return false;
  }
  auto shape = mlir::cast<ShapedType>(sq_op.getType());

    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());

                           array[i].cast<mlir::StringAttr>().getValue().str());
    } else if (llvm::isa<mlir::vhlo::BooleanV1Attr>(array[i])) {
      flex_builder->Bool(name.c_str(),
                         array[i].cast<mlir::vhlo::BooleanV1Attr>().getValue());
    } else if (llvm::isa<mlir::vhlo::StringV1Attr>(array[i])) {
      flex_builder->String(
          name.c_str(),

  }

  // Get the tensor types.
  const auto input_type = input_tensor.getType().cast<ShapedType>();
  const auto weights_type = weights_tensor.getType().cast<ShapedType>();
  const auto bias_type =
      has_bias ? bias_tensor.getType().cast<ShapedType>() : ShapedType{};

  const auto output_type = getType(0).cast<ShapedType>();

  // Folding only implemented for float tensors.

  }
}

LogicalResult SpaceToBatchNDOp::verify() {
  SpaceToBatchNDOp op = *this;
  const auto input_type = op.getInput().getType().cast<TensorType>();
  const auto block_shape_type = op.getBlockShape().getType().cast<TensorType>();
  const auto paddings_type = op.getPaddings().getType().cast<TensorType>();

  // Check that block_shape has rank 1.
  if (!IsOfRankOrUnranked(op.getBlockShape(), 1)) {

  auto input_args_have_static_shape = [&]() -> bool {
    return mlir::cast<TensorType>(input_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(kernel_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(window_strides.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(padding.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(lhs_dilation.getType()).hasStaticShape() &&