How to use the numjs.sum function in numjs

const valids = this.game.getValidMoves(canonicalBoard, 1);
      // NOTE: : Array multiplication is not matrix multiplication:
      // Python: self.Ps[s] = self.Ps[s]*valids
      this.Ps[s] = nj.multiply(this.Ps[s], valids); // # masking invalid moves
      const sum_Ps_s = nj.sum(this.Ps[s]);
      if (sum_Ps_s > 0) {
        this.Ps[s] = nj.divide(this.Ps[s], sum_Ps_s); // renormalize
      } else {
        // # if all valid moves were masked make all valid moves equally probable
        //
        // # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.
        // # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.
        console.log('All valid moves were masked, do workaround.');
        this.Ps[s] = nj.add(this.Ps[s], valids);
        this.Ps[s] = nj.divide(this.Ps[s], nj.sum(this.Ps[s]));
      }

      this.Vs[s] = valids;
      this.Ns[s] = 0;
      return v;
    }

    const valids = this.Vs[s];
    let cur_best = Number.NEGATIVE_INFINITY;
    let best_act = -1;
    const aSize = this.game.getActionSize();
    // # pick the action with the highest upper confidence bound
    for (let a = 0; a < aSize; a++) {
      // NOTE: valid is a ndarray
      if (valids.get(a) > 0) {
        const saKey = `${s};${a}`;

return -this.Es[s];
    }

    if (this.Ps.hasOwnProperty(s) == false) {
      // # leaf node
      // NOTE: Python ver.: v is ndarray type: [0.x]. qsa, too.
      // JavaScript: v is just a number value.
      const resp = this.nnet.predict(canonicalBoard);
      this.Ps[s] = resp.Ps;
      const v = resp.v;// .get(0);

      const valids = this.game.getValidMoves(canonicalBoard, 1);
      // NOTE: : Array multiplication is not matrix multiplication:
      // Python: self.Ps[s] = self.Ps[s]*valids
      this.Ps[s] = nj.multiply(this.Ps[s], valids); // # masking invalid moves
      const sum_Ps_s = nj.sum(this.Ps[s]);
      if (sum_Ps_s > 0) {
        this.Ps[s] = nj.divide(this.Ps[s], sum_Ps_s); // renormalize
      } else {
        // # if all valid moves were masked make all valid moves equally probable
        //
        // # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.
        // # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.
        console.log('All valid moves were masked, do workaround.');
        this.Ps[s] = nj.add(this.Ps[s], valids);
        this.Ps[s] = nj.divide(this.Ps[s], nj.sum(this.Ps[s]));
      }

      this.Vs[s] = valids;
      this.Ns[s] = 0;
      return v;
    }