code: def predict(self, text, verbose=False):
      # Inputs are provided through numpy array
      model_inputs = self.tokenizer(text, return_tensors="pt")
      inputs_onnx = {
          k: v.cpu().detach().numpy() for k, v in model_inputs.items()
      }
      outputs = self.model.run(None, inputs_onnx)
      softmax_preds = softmax(outputs[0])
      preds = list(zip(self.labels, softmax_preds[0]))
      return sorted(preds, key=lambda x: x[1], reverse=True)
  
imports: import torch
  from pathlib import Path
  from .onnx_helper import load_model
  from .learner_ner import group_entities
  from .data_ner import get_labels
  from transformers import AutoTokenizer
  import numpy
  import warnings
label_desc: Use image node_id 2 for calling the BertOnnxClassificationPredictor obj's underlying member method code with example usage: obj.predict(text, verbose) and returns: sorted