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    AbstractTensorHandlePtr fDiff(f_outputs[0]);

    // Calculate using the difference quotient definition:
    // (f(theta + eps) - f(theta - eps)) / (2 * eps).
    TF_RETURN_IF_ERROR(
        ops::Div(ctx, fDiff.get(), two_eps.get(), f_outputs, "diff_quotient"));
    AbstractTensorHandlePtr diff_quotient(f_outputs[0]);

    TF_Tensor* grad_tensor;
    TF_RETURN_IF_ERROR(GetValue(diff_quotient.get(), &grad_tensor));

    TF_RETURN_IF_ERROR(
        AddV2(ctx, Ones_X.get(), Conj_X.get(), &temp_output, name.c_str()));

    AbstractTensorHandlePtr Conj_XP1(temp_output);

    name = "Div_Log1p_Grad_X";
    // Calculate U / (1 + Conj(X))
    TF_RETURN_IF_ERROR(
        Div(ctx, upstream_grad, Conj_XP1.get(), &grad_inputs[0], name.c_str()));

    return absl::OkStatus();
  }
  ~Log1pGradientFunction() override {

and modulus operators (/, //, %) now match Python (flooring) semantics. This
applies to `tf.div` and `tf.mod` as well. To obtain forced integer truncation
based behaviors you can use `tf.truncatediv` and `tf.truncatemod`. *
`tf.divide()` is now the recommended division function. `tf.div()` will remain,
but its semantics do not respond to Python 3 or `from future` mechanisms. *