//
// This represents the type information with OpDef::ArgDef and any type-related
// context.
class ArgType {
 public:
  ArgType() = default;
  ArgType(const ArgType& other) = default;
  static ArgType CreateInput(const OpDef::ArgDef& arg_def);
  static ArgType CreateInputRef(const OpDef::ArgDef& arg_def);
  static ArgType CreateOutput(const OpDef::ArgDef& arg_def);

  const tensorflow::DataType data_type() const { return data_type_; }

namespace tensorflow {
namespace generator {

ArgType ArgType::CreateInput(const OpDef::ArgDef& arg_def) {
  return ArgType(arg_def, kInput);
}

ArgType ArgType::CreateInputRef(const OpDef::ArgDef& arg_def) {
  return ArgType(arg_def, kInputRef);
}

ArgType ArgType::CreateOutput(const OpDef::ArgDef& arg_def) {
  return ArgType(arg_def, kOutput);
}

ArgType::ArgType(const OpDef::ArgDef& arg_def, Kind kind)

namespace generator {

ArgSpec::ArgSpec(const OpDef::ArgDef& arg_def, ArgType arg_type, int position)
    : name_(arg_def.name()),
      description_(arg_def.description()),
      arg_type_(arg_type),
      position_(position) {}

ArgSpec ArgSpec::CreateInput(const OpDef::ArgDef& arg_def, int position) {
  if (arg_def.is_ref()) {
    return ArgSpec(arg_def, ArgType::CreateInputRef(arg_def), position);
  } else {

		off := a.BitFields.ParseSigned(i) << a.Shift
		neg := int64(-1) << (int(a.Shift) + a.BitFields.NumBits())
		return Offset(neg | off)
	}
}

type ArgType int8

const (
	TypeUnknown      ArgType = iota
	TypePCRel                // PC-relative address
	TypeLabel                // absolute address
	TypeReg                  // integer register

  const string& description() const { return description_; }
  const ArgType arg_type() const { return arg_type_; }
  const int position() const { return position_; }

 private:
  explicit ArgSpec(const OpDef::ArgDef& arg_def, ArgType arg_type,
                   int position);

  string name_;
  string description_;
  ArgType arg_type_;
  int position_;
};

}  // namespace generator

#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace generator {
namespace cpp {

class ArgTypeView {
 public:
  explicit ArgTypeView(ArgType arg_type);

  string TypeName() const;

 private:
  ArgType arg_type_;
};

}  // namespace cpp
}  // namespace generator
}  // namespace tensorflow

#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 {

	{{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))

> 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

      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`