code: def _set_generation_config(
      self, new_generation_config_dict: Dict[str, Any]
  ):
      self.generation = GenerationConfig(**new_generation_config_dict)
      # We need to manually set the pad_token_id to the tokenizer's pad_token_id for certain models like GPT and
      # CodeLlama to avoid getting an error. This workaround can be found here:
      # (https://github.com/huggingface/transformers/issues/25353#issuecomment-1669339754)
      self.generation.pad_token_id = self.tokenizer.pad_token_id
      self.max_new_tokens = self.generation.max_new_tokens
      # max input length value copied from FastChat
      # https://github.com/lm-sys/FastChat/blob/0e958b852a14f4bef5f0e9d7a5e7373477329cf2/fastchat/serve/inference.py#L183  # noqa E501
      self.max_input_length = self.context_len - self.max_new_tokens - 8
  
imports: import contextlib
  import logging
  import os
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from transformers import AutoConfig, GenerationConfig
  from ludwig.accounting.used_tokens import get_used_tokens_for_llm
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, MODEL_LLM, PREDICTIONS, TEXT, USED_TOKENS
  from ludwig.features.base_feature import ModuleWrapper, OutputFeature
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.features.text_feature import TextOutputFeature
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.modules.training_hooks import NEFTuneHook
  from ludwig.schema.features.base import BaseOutputFeatureConfig, FeatureCollection
  from ludwig.schema.model_types.llm import LLMModelConfig
  from ludwig.utils.augmentation_utils import AugmentationPipelines
  from ludwig.utils.data_utils import clear_data_cache
  from ludwig.utils.llm_quantization_utils import convert_quantized_linear_to_linear
  from ludwig.utils.llm_utils import add_left_padding, generate_merged_ids, get_context_len, get_realigned_target_and_prediction_tensors_for_inference, initialize_adapter, load_pretrained_from_config, pad_target_tensor_for_fine_tuning, remove_left_padding, to_device
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.output_feature_utils import set_output_feature_tensor
  from ludwig.utils.tokenizers import HFTokenizer
  from ludwig.utils.torch_utils import reg_loss
label_desc: Use image node_id 5 for calling the LLM obj's underlying member method code with example usage: obj._set_generation_config(new_generation_config_dict) without return types