supervised machine learning if self headerfeat __doc__ "{self header[ind]}=={true_val}elsefeat __doc__ " [{ind}]=={true_val}feat frange boolean feat ftype "booleanconds append(featelif all(isinstance(val,(int,float)for val in frange)if categorical_onlynumericaldon' make cuts def feat(ei=ind)return [ifeat __doc__ " [{ind}]conds append(featelseall numericcreate cuts of the data sorted_frange sorted(frangenum_cuts min(max_num_cuts,len(frange)cut_positions [len(frange)* //num_cuts for in range( ,num_cuts)for cut in cut_positionscutat sorted_frange[cutdef feat(eind_=indcutat=cutat)return [ind_cutat if self headerfeat __doc__ self header[ind]+"<"+str(cutatelsefeat __doc__ " ["+str(ind)+"]<"+str(cutatfeat frange boolean feat ftype "booleanconds append(feat elsecreate an indicator function for every value for val in frangedef feat(eind_=indval_=val)return [ind_=val_ if self headerfeat __doc__ self header[ind]+"=="+str(valelsefeat __doc__" ["+str(ind)+"]=="+str(valfeat frange boolean feat ftype "booleanconds append(featself conditions_cache[(max_num_cutscategorical_only)conds return conds exercise change the code so that it splits using [ind<cut instead of [indcut check boundary casessuch as elements with cuts as test casemake sure that when the range is the integers from to and you want cutsthe resulting boolean features should be [ind< and [ind< to make version june