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 typing import Optional, Tuple, Any, Dict, Iterable
  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
  from util import logsumexp, dummy_padding, prepare_pair_embeddings, one_hot_ignore_negative, batched_masked_select
label_desc: Use image node_id 12 for calling the T5Coref obj's underlying member method code with example usage: obj._resize_action_logits_bias(new_num_tokens) without return types