code: def __init__(
      self,
      model_path,
      label_path,
      model_name="model.onnx",
      multi_label=False,
      model_type="bert",
      use_fast_tokenizer=True,
      do_lower_case=True,
      device=None,
  ):
      if device is None:
          device = (
              torch.device("cuda")
              if torch.cuda.is_available()
              else torch.device("cpu")
          )
  
      self.model_path = model_path
      self.label_path = label_path
      self.multi_label = multi_label
      self.model_type = model_type
      self.do_lower_case = do_lower_case
      self.device = device
      self.labels = []
  
      # Use auto-tokenizer
      self.tokenizer = AutoTokenizer.from_pretrained(
          self.model_path, use_fast=use_fast_tokenizer
      )
  
      with open(label_path / "labels.csv", "r") as f:
          self.labels = f.read().split("\n")
  
      self.model = load_model(Path(self.model_path) / model_name)
  
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 1 to create a new BertOnnxClassificationPredictor object from inherited base classes: object with example: obj = BertOnnxClassificationPredictor(model_path, label_path, model_name, multi_label, model_type, use_fast_tokenizer, do_lower_case, device)