/* Given upstream grad U and a Log1p op: Y = log(1 + X), the gradients are:
     *
     *    dX = U / (1 + X)
     *
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    AbstractTensorHandle* X = forward_inputs_[0];

    AbstractTensorHandle* temp_output;

    // Calculate conjugate of X
    std::string name = "Conj_Log1p_Grad_X";

}

Status Log1pModel(AbstractContext* ctx,
                  absl::Span<AbstractTensorHandle* const> inputs,
                  absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Log1p(ctx, inputs[0], &outputs[0], "Log1p");
}

Status DivNoNanModel(AbstractContext* ctx,
                     absl::Span<AbstractTensorHandle* const> inputs,
                     absl::Span<AbstractTensorHandle*> outputs) {

        `tf.math.greater`, `tf.math.greater_equal`, `tf.math.igamma`,
        `tf.math.igammac`, `tf.math.invert_permutation`, `tf.math.less`,
        `tf.math.less_equal`, `tf.math.lgamma`, `tf.math.log`, `tf.math.log1p`,
        `tf.math.logical_and`, `tf.math.logical_not`, `tf.math.logical_or`,
        `tf.math.maximum`, `tf.math.minimum`, `tf.math.not_equal`,
        `tf.math.polygamma`, `tf.math.reciprocal`, `tf.math.rint`,