How to use the numjs.tanh function in numjs

function get_init_state_from_latent_vector(y) {

        if (y.constructor != Array) {
            console.log("error, the argument passed into decode must be a Javascript normal Array type.");
            y = Array.prototype.slice.call(y);
        };

        var z = nj.array(y);

        /**
         * for backward compatibility.
         */
        if (this.info.version < 6) {
            z = nj.tanh(z); // for older versions, clip the latent variable.
        }

        var init_state = nj.tanh(nj.add(nj.dot(z, enc_w), enc_b));

        var c = init_state.slice([0, dec_num_units]).clone();
        var h = init_state.slice([dec_num_units, 2 * dec_num_units]).clone();

        return [h, c];
    };

if (y.constructor != Array) {
            console.log("error, the argument passed into decode must be a Javascript normal Array type.");
            y = Array.prototype.slice.call(y);
        };

        var z = nj.array(y);

        /**
         * for backward compatibility.
         */
        if (this.info.version < 6) {
            z = nj.tanh(z); // for older versions, clip the latent variable.
        }

        var init_state = nj.tanh(nj.add(nj.dot(z, enc_w), enc_b));

        var c = init_state.slice([0, dec_num_units]).clone();
        var h = init_state.slice([dec_num_units, 2 * dec_num_units]).clone();

        return [h, c];
    };

LSTMCell.prototype.forward = function(x, h, c) {
    var concat = nj.concatenate([x, h]);
    var hidden = nj.add(nj.dot(concat, this.Wfull), this.bias);
    var num_units = this.num_units;
    var forget_bias = this.forget_bias;

    var i = nj.sigmoid(hidden.slice([0 * num_units, 1 * num_units]));
    var g = nj.tanh(hidden.slice([1 * num_units, 2 * num_units]));
    var f = nj.sigmoid(nj.add(hidden.slice([2 * num_units, 3 * num_units]), forget_bias));
    var o = nj.sigmoid(hidden.slice([3 * num_units, 4 * num_units]));

    var new_c = nj.add(nj.multiply(c, f), nj.multiply(g, i));
    var new_h = nj.multiply(nj.tanh(new_c), o);

    return [new_h, new_c];
};
LSTMCell.prototype.encode = function(sequence) {

function get_pdf(s) {
        var h = s[0];
        var NOUT = N_mixture;
        var z = nj.add(nj.dot(h, dec_output_w), dec_output_b);
        var z_pen_logits = z.slice([0, 3]);
        var z_pi = z.slice([3 + NOUT * 0, 3 + NOUT * 1]);
        var z_mu1 = z.slice([3 + NOUT * 1, 3 + NOUT * 2]);
        var z_mu2 = z.slice([3 + NOUT * 2, 3 + NOUT * 3]);
        var z_sigma1 = nj.exp(z.slice([3 + NOUT * 3, 3 + NOUT * 4]));
        var z_sigma2 = nj.exp(z.slice([3 + NOUT * 4, 3 + NOUT * 5]));
        var z_corr = nj.tanh(z.slice([3 + NOUT * 5, 3 + NOUT * 6]));
        z_pen_logits = nj.subtract(z_pen_logits, z_pen_logits.max());
        var z_pen = nj.softmax(z_pen_logits);
        z_pi = nj.subtract(z_pi, z_pi.max());
        z_pi = nj.softmax(z_pi);

        return [z_pi, z_mu1, z_mu2, z_sigma1, z_sigma2, z_corr, z_pen];
    };

LSTMCell.prototype.forward = function(x, h, c) {
    var concat = nj.concatenate([x, h]);
    var hidden = nj.add(nj.dot(concat, this.Wfull), this.bias);
    var num_units = this.num_units;
    var forget_bias = this.forget_bias;

    var i = nj.sigmoid(hidden.slice([0 * num_units, 1 * num_units]));
    var g = nj.tanh(hidden.slice([1 * num_units, 2 * num_units]));
    var f = nj.sigmoid(nj.add(hidden.slice([2 * num_units, 3 * num_units]), forget_bias));
    var o = nj.sigmoid(hidden.slice([3 * num_units, 4 * num_units]));

    var new_c = nj.add(nj.multiply(c, f), nj.multiply(g, i));
    var new_h = nj.multiply(nj.tanh(new_c), o);

    return [new_h, new_c];
};
LSTMCell.prototype.encode = function(sequence) {