code: def js_PCoA(distributions):
      """Dimension reduction via Jensen-Shannon Divergence & Principal Coordinate Analysis
      (aka Classical Multidimensional Scaling)
  
      Parameters
      ----------
      distributions : array-like, shape (`n_dists`, `k`)
          Matrix of distributions probabilities.
  
      Returns
      -------
      pcoa : array, shape (`n_dists`, 2)
      """
      dist_matrix = squareform(
          pdist(distributions, metric=_jensen_shannon)
      )
      return _pcoa(dist_matrix)
  
imports: import json
  import logging
  import numpy
  import pandas
  from collections import namedtuple
  from joblib import Parallel, delayed, cpu_count
  from scipy.stats import entropy
  from scipy.spatial.distance import pdist, squareform
  from sklearn.manifold import MDS, TSNE
  from pyLDAvis.utils import NumPyEncoder
label_desc: Use image node_id 6 for calling a global function with example usage: js_PCoA(distributions) and returns: _pcoa