import tensorflow as tf

            pi_dropout_mask_generator = DropoutMaskGenerator(pi_in_dim, hidden_sizes, model_prob=1.0 - dropout_rate)
            pi_dropout_mask_phs = pi_dropout_mask_generator.generate_dropout_mask_placeholders()

            pi, pi_reg = mlp_variational(x, pi_dropout_mask_phs, list(hidden_sizes) + [act_dim], activation, output_activation, dropout_rate)
            pi = act_limit * pi
        with tf.variable_scope('q1'):
            q1_in_ph = tf.concat([x, a], axis=-1)
            q1_in_dim = q1_in_ph.shape.as_list()[1]
            q1_dropout_mask_generator = DropoutMaskGenerator(q1_in_dim, hidden_sizes, model_prob=1.0 - dropout_rate)
            q1_dropout_mask_phs = q1_dropout_mask_generator.generate_dropout_mask_placeholders()
