added comments

    return G,vns[0][0]

def simplify_text_description_nodes(G,node_index_dict,mode="None",min_freq=1):

    """function takes our graph and simplifies text description nodes

       E.g., felouneously stealing, on the 10th Decembre,  two silver watches ----> watches

    """

    if mode == "None":

        return G, node_index_dict

    #collect all descriptions and their neighbor category

    trialnodes=[n for n in G.nodes(data=True) if isinstance(n[1]["nodeobj"],dh.TrialNode)]

    descr_nodes=[]

    mask=[]

    # we iterate over all trials

    for i,tn in enumerate(trialnodes):

        #get corresponding cat node

        catn = [n for n in G.neighbors(tn[0]) if isinstance(G.nodes[n]["nodeobj"],dh.OffenceNode)][0]

        catn=[catn,G.nodes[catn]]

        #print(catn)

        #catn=[cat]

        category=catn[1]["nodeobj"].d["category"]

        descr_vectors = None

        tid = None

        # we iterate over all neighbors of the trial

        for nb in G[tn[0]]:

            for edge_id in G[tn[0]][nb]:

                # and grab nodes which describe a offence

                if G[tn[0]][nb][edge_id]["edge_class"] == "with-offence-description":

                    #print(G.nodes[nb])

                    # we collect the noun chunk vectors

                    descr_vectors,_ = G.nodes[nb]["nodeobj"].get_noun_chunk_vectors()

                    descr_nodes.append(G.nodes[nb]["nodeobj"])

                    #descr_nodes[-1].simplify_to_direct_object()

                    tid=(tn[0],nb)  

                    Xid.append(tid)

                    for dv in descr_vectors:

                        #put noun chunk vector into training data

                        Xvector.append(dv)

                        #put label into training data

                        related_cat.append(category)

    if mode=="classifier":

        #fit a classifier to learn a mapping between noun chunks and labels

        clf=LogisticRegression()

        clf.fit(Xvector,related_cat)

    # now we can remove the textdescription nodes and insert their simplified fporms

    for i,idx in enumerate(Xid):

        if idx[1] in G:

            G.remove_node(idx[1])

            #node_index_dict.pop(descr_nodes[i])