class_docstr: use AutoModelForMaskedLM, and select the output by another layer in the lit model
code: def add_to_argparse(parser):
      BaseLitModel.add_to_argparse(parser)
      parser.add_argument(
          "--t_lambda", type=float, default=0.01, help=""
      )
      return parser
  
imports: import json
  import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from .base import BaseLitModel
  from .util import dialog_f1_eval, f1_eval, compute_f1, acc, f1_score
  from transformers.optimization import get_linear_schedule_with_warmup
  from functools import partial
label_desc: Use image node_id 9 for calling the BertLitModel obj's underlying member method code with example usage: obj.add_to_argparse(parser) and returns: parser