code: def _classify(self, data):
      preds, raw_outputs = self.cls_model.predict(data["text"].tolist())
  
      probs = nn.functional.softmax(
          torch.from_numpy(raw_outputs), dim=1
      )
      probs = torch.max(probs, dim=1).values.tolist()
      probs = [round(p, 4) for p in probs]
  
      data["pred"] = [self.id2label[p] for p in preds]
      data["prob"] = probs
      return data
  
file_path: nlpaug/nlpaug/model/lang_models/lambada.py
imports: import pandas
  import json
  import os
  from nlpaug.model.lang_models import LanguageModels
label_desc: Use image node_id 5 for calling the Lambada obj's underlying member method code with example usage: obj._classify(data) and returns: data