docstr: This method merges the LoRa layers into the base model.  This is needed if someone wants to use the base
  model as a standalone model.  The implementation calls merge_and_unload() of the underlying LoraModel class
  (in peft).
  
  Args:
      progressbar (bool): whether to show a progressbar indicating the unload and merge process
docstr_tok: ["This","method","merges","the","LoRa","layers","into","the","base","model",".","This","is","needed","if","someone","wants","to","use","the","base","model","as","a","standalone","model",".","The","implementation","calls","merge_and_unload","(",")","of","the","underlying","LoraModel","class","(","in","peft",")",".","Args",":","progressbar","(","bool",")",":","whether","to","show","a","progressbar","indicating","the","unload","and","merge","process"]
code: def merge_and_unload(self, progressbar: bool = False) -> None:
      """This method merges the LoRa layers into the base model.  This is needed if someone wants to use the base
      model as a standalone model.  The implementation calls merge_and_unload() of the underlying LoraModel class
      (in peft).
  
      Args:
          progressbar (bool): whether to show a progressbar indicating the unload and merge process
      """
      from peft import LoraModel
  
      if isinstance(self.model.base_model, LoraModel):
          self.model.base_model.merge_and_unload(
              progressbar=progressbar
          )
      else:
          raise ValueError(
              "This operation requires an LLM model trained with a LoRA adapter."
          )
  
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 15 for calling the LLM obj's underlying member method code with example usage: obj.merge_and_unload(progressbar) without return types