// elements. It returns a new slice of inline elements with links added.
func addSymbolLinksInlines(ins []md.Inline, defaultPackage string) []md.Inline {
	ins = splitAtBrackets(ins)
	var res []md.Inline
	for i := 0; i < len(ins); i++ {
		if txt := symbolLinkText(i, ins); txt != "" {
			link, ok := symbolLink(txt, defaultPackage)
			if ok {
				res = append(res, link)
				i += 2
				continue
			}

      "std::vector<int>", "GetSparseOperands", (ins)
    >,
    InterfaceMethod<
      [{Returns the supported block size of float sparse operands.}],
      "std::vector<std::vector<int>>", "GetFloatBlockSize", (ins)
    >,
    InterfaceMethod<
      [{Returns the supported block size of quantized sparse operands.}],
      "std::vector<std::vector<int>>", "GetQuantizedBlockSize", (ins)
    >,
  ];
}

      "int", "GetAffineOperandIndex",
      (ins), [{}], [{return 1;}]>,
    InterfaceMethod<
      [{Returns whether narrow range is required for the affine operand.}],
      "bool", "RequiredNarrowRangeAffineOperand",
      (ins), [{}], [{return true;}]>,
    InterfaceMethod<
      [{Returns quantization dim for the affine operand.}],
      "int", "GetQuantizationDimIndex",
      (ins)>,
    InterfaceMethod<

def TF_UniformQuantizedDotGeneralOp : TF_Op<"UniformQuantizedDotGeneral", [Pure]> {
  // TODO(b/230804708): Improve the operator description.
  let summary = "Quantized dot general operator.";

  let arguments = (ins
    TensorOf<[TF_Float32, TF_Qint8]>:$lhs,
    TensorOf<[TF_Qint8]>:$rhs,
    TF_Float32Tensor:$lhs_scales,
    TF_Int32Tensor:$lhs_zps,
    TF_Float32Tensor:$rhs_scales,
    TF_Int32Tensor:$rhs_zps,

  let description = [{
    Setting a tensor value in TFRT runtime's static resource manager, using
    index as its identifier. It can be retrieved by _TfrtGetResource using the
    same index.
  }];

  let arguments = (ins
    TF_Tensor:$arg,
    I64Attr:$index
  );
}

def TF__TfrtGetResourceOp : TF_Op<"_TfrtGetResource",
    [Pure, TF_NoConstantFold, DeclareOpInterfaceMethods<TF_ResourceHandleAllocatorInterface>]> {

  let description = [{
    Transfer a CPU tensor to device.

    Example:
      %device_tensor = gpurt.transfer_to_device %cpu_tensor
  }];

  let arguments = (ins TFTensorType);
  let results = (outs TFTensorType);
  let assemblyFormat = "operands attr-dict";
}

// TODO(b/260267885): We may add a device argument when we want to support
// GPU MIG.

    Await a tensor from a !mlrt.future.

    $future: A value of type !mlrt.future. The underlying value must be a tensorflow tensor.

    $result: a tensorflow tensor.
  }];

  let arguments = (ins
    MlrtFutureType:$future
  );

  let results = (outs
    TF_Tensor:$result
  );
}

// tf_mlrt.tf_promise takes a tensorflow Tensor. It is a fake op that is only

    private val directDependentsByKtModule: Map<KtModule, Set<KtModule>> by lazy {
        // Direct dependencies should be computed lazily, because the built-ins module will be reachable via module dependencies. Getting
        // the built-ins module relies on the built-ins session, which may depend on services that are registered after
        // `KtStaticModuleDependentsProvider`.
        buildDependentsMap(modules) { it.allDirectDependencies() }

	case *md.List:
		dumpBlocks(b.Items, depth+1)
	case *md.Item:
		dumpBlocks(b.Blocks, depth+1)
	default:
		// TODO(jba): additional cases as needed.
	}
}

func dumpInlines(ins []md.Inline, depth int) {
	for _, in := range ins {
		switch in := in.(type) {
		case *md.Plain:
			dprintf(depth, "Plain(%q)\n", in.Text)
		case *md.Code:
			dprintf(depth, "Code(%q)\n", in.Text)
		case *md.Link:
			dprintf(depth, "Link:\n")

        }

        return new StreamResponse(osddFile.getName()).contentType(contentType + "; charset=" + encoding).stream(out -> {
            try (InputStream ins = new FileInputStream(osddFile)) {
                out.write(ins);
            }
        });
    }

    public void setOsddPath(final String osddPath) {
        this.osddPath = osddPath;
    }