code: def from_pretrained(*args, **kwargs):
      old_create_new_module = LoraModel._create_new_module
      LoraModel._create_new_module = staticmethod(
          functools.partial(_create_new_module, old_create_new_module)
      )
      from peft import PeftModel
  
      try:
          model = PeftModel.from_pretrained(*args, **kwargs)
      finally:
          LoraModel._create_new_module = old_create_new_module
  
      return model
  
imports: import torch
  from torch.nn import Linear, Embedding
  from bigdl.llm.transformers.low_bit_linear import LowBitLinear, BF16Linear, get_qk_size
  from peft.tuners.lora import LoraLayer
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.transformers.utils import get_autocast_dtype
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  import functools
  from bigdl.llm.transformers import training_patch
  from peft.tuners.lora import LoraModel
  from peft.tuners.lora import LoraConfig
  from dataclasses import dataclass, field
  from peft.mapping import PEFT_TYPE_TO_CONFIG_MAPPING
label_desc: Use image node_id 1 for calling the PeftModel obj's underlying member method code with example usage: obj.from_pretrained() and returns: model