import tensorflow as tf

                    # check_shape([rho, distribution_f, q_value], [[self.n_envs * self.n_steps, self.n_act]] * 2)

                    # Truncated importance sampling
                    adv = qret - value
                    log_f = tf.log(f_i + eps)
                    # [n_envs * n_steps]
                    gain_f = log_f * tf.stop_gradient(adv * tf.minimum(self.correction_term, rho_i))
                    loss_f = -tf.reduce_mean(gain_f)

                    # Bias correction for the truncation
                    adv_bc = (q_value - tf.reshape(value, [self.n_envs * self.n_steps, 1]))  # [n_envs * n_steps, n_act]

                    # check_shape([adv_bc, log_f_bc], [[self.n_envs * self.n_steps, self.n_act]] * 2)
                    if continuous:
