code: def loftq_init(self, adapter_name):
      from peft.utils.loftq_utils import loftq_init
  
      weight = self.get_base_layer().weight
      kwargs = {
          "num_bits": self.kwargs.get("loftq_bits", 4),
          "reduced_rank": self.r[adapter_name],
          "num_iter": self.kwargs.get("loftq_iter", 1),
      }
  
      qweight, lora_A, lora_B = loftq_init(weight, **kwargs)
      if adapter_name in self.lora_A.keys():
          # initialize A the same way as the default for nn.Linear and B to zero
          self.lora_A[adapter_name].weight.data = lora_A
          self.lora_B[adapter_name].weight.data = lora_B
      if adapter_name in self.lora_embedding_A.keys():
          # initialize a the same way as the default for nn.linear and b to zero
          self.lora_embedding_A[adapter_name].weight.data = lora_A
          self.lora_embedding_B[adapter_name].weight.data = lora_B
      self.get_base_layer().weight.data = qweight
  
imports: import math
  import warnings
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.pytorch_utils import Conv1D
  from peft.tuners.tuners_utils import BaseTunerLayer
  from peft.utils.other import transpose
  from .config import LoraConfig
label_desc: Use image node_id 4 for calling the LoraLayer obj's underlying member method code with example usage: obj.loftq_init(adapter_name) without return types