code: def find_all_linear_names(model):
      cls = (
          bnb.nn.Linear4bit,
          bnb.nn.Linear8bitLt,
          torch.nn.Linear,
          QuantLinear,
      )
      lora_module_names = set()
      for name, module in model.named_modules():
          if (
              isinstance(module, cls)
              or "Linear" in module.__class__.__name__
              and module.__class__.__name__
              not in ("LlamaLinearScalingRotaryEmbedding",)
          ):
              names = name.split(".")
              lora_module_names.add(
                  names[0] if len(names) == 1 else names[-1]
              )
  
      if "lm_head" in lora_module_names:  # needed for 16-bit
          lora_module_names.remove("lm_head")
  
      return list(lora_module_names)
  
imports: import logging
  import math
  import os
  from typing import Any, Optional, Tuple, Union
  import addict
  import bitsandbytes
  import torch
  import transformers
  from optimum.bettertransformer import BetterTransformer
  from peft import PeftConfig, prepare_model_for_kbit_training
  from peft.tuners.lora import QuantLinear
  from transformers import AddedToken, AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GPTQConfig, PreTrainedModel, PreTrainedTokenizerBase
  from transformers.deepspeed import is_deepspeed_zero3_enabled
  from axolotl.models.mamba import fix_mamba_attn_for_loss
  from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
  from axolotl.utils.bench import log_gpu_memory_usage
  from axolotl.utils.chat_templates import chat_templates
  from axolotl.utils.dict import DictDefault
  from axolotl.utils.lora_embeddings import get_linear_embedding_layers
label_desc: Use image node_id 7 for calling a global function with example usage: find_all_linear_names(model) and returns: list