code: def __init__(
      self,
      r: int,
      lora_alpha: int,
      lora_dropout: float,
      merge_weights: bool,
  ):
      self.r = r
      self.lora_alpha = lora_alpha
      # Optional dropout
      if lora_dropout > 0.0:
          self.lora_dropout = nn.Dropout(p=lora_dropout)
      else:
          self.lora_dropout = lambda x: x
      # Mark the weight as unmerged
      self.merged = False
      self.merge_weights = merge_weights
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import math
  from torch.utils.checkpoint import checkpoint
  from typing import Dict
  import os
label_desc: Use image node_id 1 to create a new LoRALayer object  with example: obj = LoRALayer(r, lora_alpha, lora_dropout, merge_weights)