 How to Use  ```py class UDgoeswith(object):   def __init__(self,bert):     from transformers import AutoTokenizer,AutoModelForTokenClassification     self.tokenizer=AutoTokenizer.from_pretrained(bert)     self.model=AutoModelForTokenClassification.from_pretrained(bert)   def __call__(self,text):     import numpy,torch,ufal.chu_liu_edmonds     w=self.tokenizer(text,return_offsets_mapping=True)     v=w["input_ids"]     x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]     with torch.no_grad():       e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]     r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]     e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)     g=self.model.config.label2id["X|_|goeswith"]     r=numpy.tri(e.shape[0])     for i in range(e.shape[0]):       for j in range(i+2,e.shape[1]):         r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1     e[:,:,g]+=numpy.where(r==0,0,numpy.nan)     m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)     m[1:,1:]=numpy.nanmax(e,axis=2).transpose()     p=numpy.zeros(m.shape)     p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()     for i in range(1,m.shape[0]):       m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]     h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]     if [0 for i in h if i==0]!=[0]:       m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)       m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]       h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]     u="