class_docstr: Reranker models based on huggingface/transformers library
docstr: :param query:
  :param choices:
  :return: logits
code: def get_logits(self, query: str, choices: List[str]):
      """
      :param query:
      :param choices:
      :return: logits
      """
      input_ids, attention_mask, token_type_ids = self.encode(
          query, choices
      )
  
      with torch.no_grad():
          logits = self.rerank_model(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
          )[0]
          logits = logits.detach().cpu().numpy()
  
          return logits
  
file_path: nboost/nboost/plugins/rerank/transformers.py
imports: from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from typing import List
  import torch.nn
  import torch
  from nboost.plugins.rerank.base import RerankModelPlugin
  from nboost import defaults
  from nboost.logger import set_logger
label_desc: Use image node_id 2 for calling the PtTransformersRerankPlugin obj's underlying member method code with example usage: obj.get_logits(query, choices) and returns: logits