code: def reset_lora_parameters(self, adapter_name, init_lora_weights):
      if init_lora_weights is False:
          return
  
      if adapter_name in self.lora_A.keys():
          if init_lora_weights is True:
              # initialize A the same way as the default for nn.Linear and B to zero
              # https://github.com/microsoft/LoRA/blob/a0a92e0f26c067cf94747bdbf1ce73793fa44d19/loralib/layers.py#L124
              nn.init.kaiming_uniform_(
                  self.lora_A[adapter_name].weight, a=math.sqrt(5)
              )
          elif init_lora_weights.lower() == "gaussian":
              nn.init.normal_(
                  self.lora_A[adapter_name].weight,
                  std=1 / self.r[adapter_name],
              )
          else:
              raise ValueError(
                  f"Unknown initialization {init_lora_weights=}"
              )
          nn.init.zeros_(self.lora_B[adapter_name].weight)
      if adapter_name in self.lora_embedding_A.keys():
          # initialize a the same way as the default for nn.linear and b to zero
          nn.init.zeros_(self.lora_embedding_A[adapter_name])
          nn.init.normal_(self.lora_embedding_B[adapter_name])
  
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 3 for calling the LoraLayer obj's underlying member method code with example usage: obj.reset_lora_parameters(adapter_name, init_lora_weights) without return types