code: def suggest_periods(ys) -> List[float]:
      """Suggest a few periods for the time series."""
      f, pxx = scipy.signal.periodogram(ys)
  
      top5_powers, top5_indices = jax.lax.top_k(pxx, 5)
      top5_power = jnp.sum(top5_powers)
      best_indices = [
          i for i in top5_indices if pxx[i] > 0.05 * top5_power
      ]
      # Sort in descending order so the best periods are first.
      best_indices.sort(reverse=True, key=lambda i: pxx[i])
      return [
          1.0 / f[i] for i in best_indices if 1.0 / f[i] < 0.6 * len(ys)
      ]
  
file_path: probability/tensorflow_probability/python/experimental/autobnn/util.py
imports: from typing import Any, Callable, List, Tuple
  import jax
  import jax.numpy
  import scipy
  from tensorflow_probability.python.experimental.autobnn import bnn
  from tensorflow_probability.substrates.jax.distributions import distribution
label_desc: Use image node_id 2 for calling a global function with example usage: suggest_periods(ys) and returns: unknown