code: def _topic_coordinates(
      mds, topic_term_dists, topic_proportion, start_index=1
  ):
      K = topic_term_dists.shape[0]
      mds_res = mds(topic_term_dists)
      assert mds_res.shape == (K, 2)
      mds_df = pd.DataFrame(
          {
              "x": mds_res[:, 0],
              "y": mds_res[:, 1],
              "topics": range(start_index, K + start_index),
              "cluster": 1,
              "Freq": topic_proportion * 100,
          }
      )
      # note: cluster (should?) be deprecated soon. See: https://github.com/cpsievert/LDAvis/issues/26
      return mds_df
  
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 11 for calling a global function with example usage: _topic_coordinates(mds, topic_term_dists, topic_proportion, start_index) and returns: mds_df