code: def consensus_tree(trees, names=None, cutoff=0.0):
      
  
      ## find which clades occured with freq > cutoff
      namedict, clade_counts = _find_clades(trees, names=names)
  
      ## filter out the < cutoff clades
      fclade_counts = _filter_clades(clade_counts, cutoff)
  
      ## build tree
      consens_tree, _ = _build_trees(fclade_counts, namedict)
      ## make sure no singleton nodes were left behind
      return consens_tree, clade_counts
docs: An extended majority rule consensus function for ete3. 
      Modelled on the similar function from scikit-bio tree module. If 
      cutoff=0.5 then it is a normal majority rule consensus, while if 
      cutoff=0.0 then subsequent non-conflicting clades are added to the tree.