code: def prepare_inputs_for_generation(
      self, input_ids, state=None, inputs_embeds=None, **kwargs
  ):
      # only last token for inputs_ids if the state is passed along.
      if state is not None:
          input_ids = input_ids[:, -1].unsqueeze(-1)
  
      # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
      if inputs_embeds is not None and state is None:
          model_inputs = {"inputs_embeds": inputs_embeds}
      else:
          model_inputs = {"input_ids": input_ids}
  
      model_inputs["state"] = state
      return model_inputs
  
imports: import math
  from dataclasses import dataclass
  from pathlib import Path
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_bitsandbytes_available, is_ninja_available, is_torch_cuda_available, logging
  from .configuration_rwkv import RwkvConfig
label_desc: Use image node_id 4 for calling the RwkvForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, state, inputs_embeds) and returns: model_inputs