code: def weighted_sum_of_all(
      time_series_xs: Array,
      time_series_ys: Array,
      depth: int = 2,
      width: int = 50,
      alpha: float = 1.0,
  ) -> bnn.BNN:
      """Return a weighted sum of all BNNs of the given depth."""
      periods = util.suggest_periods(time_series_ys)
  
      all_bnns = list_of_all(
          time_series_xs, depth, width, periods, False
      )
  
      return operators.WeightedSum(bnns=tuple(all_bnns), alpha=alpha)
  
file_path: probability/tensorflow_probability/python/experimental/autobnn/bnn_tree.py
imports: from typing import Iterable, List
  from flax import linen
  import jax
  import jax.numpy
  from tensorflow_probability.python.experimental.autobnn import bnn
  from tensorflow_probability.python.experimental.autobnn import kernels
  from tensorflow_probability.python.experimental.autobnn import operators
  from tensorflow_probability.python.experimental.autobnn import util
label_desc: Use image node_id 2 for calling a global function with example usage: weighted_sum_of_all(time_series_xs, time_series_ys, depth, width, alpha) and returns: operators