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;
        }

  op_name = SeparateNamespaces(api_def.endpoint(0).name());
  InferOpAttributes(graph_op_def, &inferred_input_attrs);
  has_optional_attrs = HasOptionalAttrs(api_def, inferred_input_attrs);
  arg_types.push_back("const ::tensorflow::Scope&");
  arg_names.push_back("scope");

  if (graph_op_def.has_deprecation()) {
    if (!api_def.summary().empty()) {
      comment = strings::StrCat(api_def.summary(), "\n");

            BackwardsConstAnalysis(**subgraph, &const_args,
                                   /*compile_time_const_nodes=*/nullptr, flr));

        DataTypeVector arg_types(num_args);
        TF_RETURN_IF_ERROR(GetArgTypes(**subgraph, &arg_types));

        // Compute a permutation of the arguments such that the constant
        // arguments are first.
        const int num_consts =

                              func::FuncOp entry_func_op,
                              TF::XlaCallModuleOp xla_call_module_op) {
  SmallVector<Type> arg_types;
  SmallVector<Location> arg_locs;
  for (const Value arg : xla_call_module_op.getArgs()) {
    arg_types.push_back(arg.getType());
    arg_locs.push_back(arg.getLoc());
  }

  SmallVector<Type> output_types;

		}
		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

                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) {

};

// Updates func's type according to its current arguments and return values.
void UpdateFuncType(func::FuncOp func) {
  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()));
}

  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Tconstants", &constant_types));

  DataTypeVector arg_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Targs", &arg_types));

  int num_resources;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Nresources", &num_resources));

    ret_operands.push_back(operand->getValue());
  }
  builder_.create<func::ReturnOp>(func_.getLoc(), ret_operands);

  auto arg_types = body.getArgumentTypes();
  auto result_types = body.getTerminator()->getOperandTypes();
  func_.setType(FunctionType::get(func_.getContext(), arg_types, result_types));
  *f = new MlirFunction(std::move(context_), std::move(module_), func_);
  return absl::OkStatus();
}

extern "C" {

};

// Updates func argument type to have the updated input shape.
void UpdateFuncType(func::FuncOp func) {
  auto arg_types = func.front().getArgumentTypes();
  auto result_types = func.front().getTerminator()->getOperandTypes();
  func.setType(FunctionType::get(func.getContext(), arg_types, result_types));
}

void HandleFuncOp(Operation* op) {
  auto func = llvm::cast<func::FuncOp>(op);
  UpdateFuncType(func);
}