docs: private double getLearningRate(int i) {
          if (gradSumSquares[i] < 0) {
              throw new RuntimeException("Gradient sum of squares entry is < 0: " + gradSumSquares[i]);
          }
          double learningRate = prm.eta / (prm.constantAddend + Math.sqrt(gradSumSquares[i]));
          assert !Double.isNaN(learningRate);
          if (learningRate == Double.POSITIVE_INFINITY) {
              if (gradSumSquares[i] != 0.0) {
                  log.warn("Gradient was non-zero but learningRate hit positive infinity: " + gradSumSquares[i]);
              }
              // Just return zero. The gradient is probably 0.0.
              return 0.0;
          }
          return learningRate;
      }