auto arg0 = ops::_Arg(scope.WithOpName("a_0_arg"), DT_INT32, 0);
  auto arg1 = ops::_Arg(scope.WithOpName("b_0_arg"), DT_FLOAT, 1);
  auto arg2 = ops::_Arg(scope.WithOpName("c_0_arg"), DT_INT32, 2);
  auto arg3 = ops::_Arg(scope.WithOpName("d_0_arg"), DT_FLOAT, 3);

  auto arg4 = ops::_Arg(scope.WithOpName("u_0_arg"), DT_RESOURCE, 4);
  auto arg5 = ops::_Arg(scope.WithOpName("v_0_arg"), DT_RESOURCE, 5);

      {
          {{"G"}, "BinaryTest", {"e_0_arg", "f_0_arg"}},
          {{"I"},
           "BinaryTest",
           {"f_0_arg", "outside_compilation_O1_host_compute:outputs:0"}},
          {{"outside_compilation_O1_host_compute"},
           "XlaHostCompute",
           {"d_0_arg", "G:o:0"},
           {{"Tinputs", absl::Span<const DataType>({DT_FLOAT, DT_FLOAT})},

    // If up with end up with
    auto dq_op = dyn_cast_or_null<quantfork::DequantizeCastOp>(
        q_op.getOperand().getDefiningOp());
    if (!dq_op) {
      return;
    }
    auto dq_arg = dq_op.getOperand();

    if (!dq_arg.hasOneUse()) {
      // The initial quantization is used someplace else ... so it might be
      // reasonable for it to requantized for another purpose.

    // If up with end up with
    auto dq_op = dyn_cast_or_null<quantfork::DequantizeCastOp>(
        q_op.getOperand().getDefiningOp());
    if (!dq_op) {
      return;
    }
    auto dq_arg = dq_op.getOperand();

    if (!dq_arg.hasOneUse()) {
      // The initial quantization is used someplace else ... so it might be
      // reasonable for it to requantized for another purpose.

            .NewSubScope(child_scope_name);

    if (dx != nullptr) {
      std::vector<tensorflow::Output> dx_arg = OutputsFromTFOutputs(dx, ny);
      status->status =
          AddSymbolicGradients(scope, y_arg, x_arg, dx_arg, &dy_arg);
    } else {
      status->status = AddSymbolicGradients(scope, y_arg, x_arg, &dy_arg);
    }

    // Update g->name_map with the name_map from the scope, which will contain