code: def _resize_action_logits_bias(self, new_num_tokens: int) -> None:
      old_num_tokens = self.action_logits_bias.shape[-1]
      if new_num_tokens <= old_num_tokens:
          new_bias = self.action_logits_bias[:, :new_num_tokens]
      else:
          extra_bias = torch.zeros(
              (1, new_num_tokens - old_num_tokens), device=self.device
          )
          new_bias = torch.cat(
              [self.action_logits_bias, extra_bias], dim=1
          )
      self.register_buffer("action_logits_bias", new_bias)
  
imports: import sys
  import os
  from ctypes import sizeof
  import math
  from typing import Optional, Tuple, Any, Dict, Iterable, List
  import logging
  import torch
  import torch.utils.checkpoint
  from torch import nn
  import torch.nn.init
  import torch.nn.functional
  from transformers.models.t5.modeling_t5 import T5PreTrainedModel, T5Model, T5_INPUTS_DOCSTRING, _CONFIG_FOR_DOC
  from transformers.file_utils import add_start_docstrings_to_model_forward, replace_return_docstrings
  from .modeling_outputs import MySeq2SeqLMOutput
  import util
  from util import batched_masked_select, logsumexp
label_desc: Use image node_id 12 for calling the T5NER obj's underlying member method code with example usage: obj._resize_action_logits_bias(new_num_tokens) without return types