code: def resize_token_embeddings(
      self,
      new_num_tokens: int,
      pad_to_multiple_of: Optional[int] = None,
  ) -> nn.Embedding:
      new_embeddings = super().resize_token_embeddings(
          new_num_tokens, pad_to_multiple_of
      )
      self._resize_final_logits_bias(new_embeddings.weight.shape[0])
      return new_embeddings
  
imports: import copy
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from ...activations import ACT2FN
  from ...modeling_outputs import MoECausalLMOutputWithPast, MoEModelOutputWithPastAndCrossAttentions
  from ...modeling_utils import PreTrainedModel
  from ...utils import DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging
  from .configuration_gptsan_japanese import GPTSanJapaneseConfig
label_desc: Use image node_id 5 for calling the GPTSanJapaneseForConditionalGeneration obj's underlying member method code with example usage: obj.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) and returns: new_embeddings