class_docstr: use AutoModelForMaskedLM, and select the output by another layer in the lit model
code: def test_epoch_end(self, outputs):
      logits = np.concatenate([o["test_logits"] for o in outputs])
      labels = np.concatenate([o["test_labels"] for o in outputs])
  
      f1 = self.eval_fn(logits, labels)["f1"]
      # print("Test/f1: ", f1)
      return f1
  
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 8 for calling the BertLitModel obj's underlying member method code with example usage: obj.test_epoch_end(outputs) and returns: f1