How to use the @tensorflow/tfjs-node-gpu.memory function in @tensorflow/tfjs-node-gpu

`dLoss = ${dLoss[0].toFixed(6)}, gLoss = ${gLoss[0].toFixed(6)}`);
      if (logWriter != null) {
        logWriter.scalar('dLoss', dLoss[0], step);
        logWriter.scalar('gLoss', gLoss[0], step);
        step++;
      }

      // Assert on no memory leak.
      // TODO(cais): Remove this check once the current memory leak in
      //   tfjs-node and tfjs-node-gpu is fixed.
      if (numTensors == null) {
        numTensors = tf.memory().numTensors;
      } else {
        tf.util.assert(
            tf.memory().numTensors === numTensors,
            `Leaked ${tf.memory().numTensors - numTensors} tensors`);
      }
    }

    await generator.save(saveURL);
    console.log(
        `epoch ${epoch + 1} elapsed time: ` +
        `${((tf.util.now() - tBatchBegin) / 1e3).toFixed(1)} s`);
    console.log(`Saved generator model to: ${saveURL}\n`);
  }

  // Write metadata to disk to indicate the end of the training.
  fs.writeFileSync(
      metadataPath,
      JSON.stringify(makeMetadata(args.epochs, args.epochs, true)));
}

console.log(
          `epoch ${epoch + 1}/${args.epochs} batch ${batch + 1}/${
              numBatches}: ` +
          `dLoss = ${dLoss[0].toFixed(6)}, gLoss = ${gLoss[0].toFixed(6)}`);
      if (logWriter != null) {
        logWriter.scalar('dLoss', dLoss[0], step);
        logWriter.scalar('gLoss', gLoss[0], step);
        step++;
      }

      // Assert on no memory leak.
      // TODO(cais): Remove this check once the current memory leak in
      //   tfjs-node and tfjs-node-gpu is fixed.
      if (numTensors == null) {
        numTensors = tf.memory().numTensors;
      } else {
        tf.util.assert(
            tf.memory().numTensors === numTensors,
            `Leaked ${tf.memory().numTensors - numTensors} tensors`);
      }
    }

    await generator.save(saveURL);
    console.log(
        `epoch ${epoch + 1} elapsed time: ` +
        `${((tf.util.now() - tBatchBegin) / 1e3).toFixed(1)} s`);
    console.log(`Saved generator model to: ${saveURL}\n`);
  }

  // Write metadata to disk to indicate the end of the training.
  fs.writeFileSync(