code: def predict_batch(self, texts, group=True, exclude_entities=["O"]):
      predictions = []
  
      for text in texts:
          pred = self.predict(
              text, group=group, exclude_entities=exclude_entities
          )
          if pred:
              predictions.append({"text": text, "results": pred})
  
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 3 for calling the BertNERPredictor obj's underlying member method code with example usage: obj.predict_batch(texts, group, exclude_entities) without return types