code: def sentence_to_sparse_vectors(sentence):
      vects = dlib.sparse_vectors()
      has_cap = dlib.sparse_vector()
      no_cap = dlib.sparse_vector()
      # make has_cap equivalent to dlib.vector([1])
      has_cap.append(dlib.pair(0, 1))
  
      # Since we didn't add anything to no_cap it is equivalent to
      # dlib.vector([0])
      for word in sentence.split():
          if word[0].isupper():
              vects.append(has_cap)
          else:
              vects.append(no_cap)
      return vects
  
imports: import sys
  import dlib
label_desc: Use image node_id 2 for calling a global function with example usage: sentence_to_sparse_vectors(sentence) and returns: vects