#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 (arg_type_.is_list()) {
      return "absl::Span<AbstractTensorHandle* const>";
    } else {

		fnname = "makeslice"
		argtype = types.Types[types.TINT]
	}
	fn := typecheck.LookupRuntime(fnname)
	ptr := mkcall1(fn, types.Types[types.TUNSAFEPTR], init, reflectdata.MakeSliceElemRType(base.Pos, n), typecheck.Conv(len, argtype), typecheck.Conv(cap, argtype))
	ptr.MarkNonNil()
	len = typecheck.Conv(len, types.Types[types.TINT])

	{{range .}}
	<hr>
	Service {{.Name}}
	<hr>
		<table>
		<th align=center>Method</th><th align=center>Calls</th>
		{{range .Method}}
			<tr>
			<td align=left font=fixed>{{.Name}}({{.Type.ArgType}}, {{.Type.ReplyType}}) error</td>
			<td align=center>{{.Type.NumCalls}}</td>
			</tr>
		{{end}}
		</table>
	{{end}}
	</body>
	</html>`

var debug = template.Must(template.New("RPC debug").Parse(debugText))

        Type methodType = Type.getMethodType(desc);
        maybeAddDependentType(types, methodType.getReturnType());
        for (Type argType : methodType.getArgumentTypes()) {
            maybeAddDependentType(types, argType);
        }
        return new MethodVisitor(types);
    }

    @Override
    public org.objectweb.asm.AnnotationVisitor visitAnnotation(String desc, boolean visible) {

            Class[] classes = new Class[arguments.length];
            for (int i = 0; i < arguments.length; i++) {
                Object argType = arguments[i];
                if (argType == null) {
                    classes[i] = null;
                } else {
                    classes[i] = argType.getClass();
                }
            }
            return classes;
        }

		}
		return v
	}

	// Special case for builtin and/or, which short-circuit.
	if isBuiltin && (name == "and" || name == "or") {
		argType := typ.In(0)
		var v reflect.Value
		for _, arg := range args {
			v = s.evalArg(dot, argType, arg).Interface().(reflect.Value)
			if truth(v) == (name == "or") {
				// This value was already unwrapped
				// by the .Interface().(reflect.Value).
				return v

> parsed as "list(type)". Other `AttrDef` messages are not types, but instead
> argument-like modifiers. In contrast, the generator model `ArgSpec` contains a
> resolved `ArgType` which provides a boolean `is_list()` method directly, and
> the model `OpSpec` provides a list of only the argument-like attributes. In
> addition to convenience, this should aid consistency between generated code in

                for (int typeVar = addNameParameter ? 1 : 0, stackVar = addNameParameter ? 2 : 1; typeVar < paramTypes.size(); ++typeVar) {
                    Type argType = paramTypes.get(typeVar);
                    _ILOAD_OF(argType, stackVar);
                    stackVar += argType.getSize();
                }
                _INVOKESPECIAL(superclassType, "<init>", superMethodDescriptor);

                if (addNameParameter) {

        final StringBuilder buf = new StringBuilder(100);
        buf.append(methodName).append("(");
        if (argTypes != null && argTypes.length > 0) {
            for (final Class<?> argType : argTypes) {
                buf.append(argType.getName()).append(", ");
            }
            buf.setLength(buf.length() - 2);
        }
        buf.append(")");
        return new String(buf);
    }

    /**

      kTfSavedModelIndexPathAttr, builder.getArrayAttr({file_prefix_attr}))});

  const int insert_idx = func_op.getNumArguments();

  func_op.insertArgument(insert_idx, /*argType=*/filename_op_type, arg_attrs,
                         NameLoc::get(file_prefix_attr));

  return func_op.getArgument(insert_idx);
}

// Creates a 1D string array constant for "tensor_names" input of `RestoreV2`