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

      arg.setType(resultType);
    }
    llvm::SmallVector<Type, 8> arg_types;
    for (auto arg : func.getArguments()) arg_types.push_back(arg.getType());
    func.setType(
        FunctionType::get(func.getContext(), arg_types,
                          func.front().getTerminator()->getOperandTypes()));
    return WalkResult::advance();
  });

#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))

  // Create the kernel.
  Device* dev = flr->device();
  Status s;
  auto props = std::make_shared<NodeProperties>(
      &fbody->record->fdef().signature(), node_def, fbody->arg_types,
      fbody->ret_types);
  OpKernelConstruction construction(DeviceType(dev->device_type()), dev,
                                    dev->GetAllocator(AllocatorAttributes()),

  string GetOpAttrStruct() const;
  string GetConstructorDecl(StringPiece op_name_prefix,
                            bool include_attr) const;

  string op_name;
  std::vector<string> arg_types;
  std::vector<string> arg_names;
  std::vector<string> output_types;
  std::vector<string> output_names;
  std::vector<bool> is_list_output;
  bool has_optional_attrs;
  string comment;

> 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`