code: def create_markov_matrix_discrete(weights_matrix, threshold):
      discrete_weights_matrix = np.zeros(weights_matrix.shape)
      ixs = np.where(weights_matrix >= threshold)
      discrete_weights_matrix[ixs] = 1
  
      return create_markov_matrix(discrete_weights_matrix)
  
file_path: sentence-transformers/examples/applications/text-summarization/LexRank.py
imports: import numpy
  from scipy.sparse.csgraph import connected_components
  from scipy.special import softmax
  import logging
label_desc: Use image node_id 5 for calling a global function with example usage: create_markov_matrix_discrete(weights_matrix, threshold) and returns: create_markov_matrix