code: def create_adaptation(
      efficient_finetune: str,
      layer: nn.Module,
      lora_r: int,
      lora_alpha: int,
  ):
      """
      Creates a model adaptation module (IA3, LoRA, IA3_LoRA) given a linear layer.
  
      Parameters
      ----------
      efficient_finetune
          Name of the adaptation module.
      layer
         The layer the adaptation module should be applied to.
      lora_r
          The rank r of the low-rank decomposition.
      lora_alpha
          The scaling factor. Can be set to same value as r in
          most cases, as initialization is scaled already.
      filter
          Apply loRA only to linear layers filtered by name (e.g. "query.").
          If None, loRA is applied to all linear Layers in module.
      module_filter
          Apply loRA only to modules filtered by name (e.g. ".*EncDecAttention|.*DenseReluDense")
          If None, loRA is considered for all modules
  
      Returns
      -------
      Model with injected LoRA modules.
      """
      if "ia3_lora" in efficient_finetune:
          return IA3LoRALinear(
              layer.in_features,
              layer.out_features,
              r=lora_r,
              lora_alpha=lora_alpha,
              merge_weights=False,
          )
      elif "ia3" in efficient_finetune:
          return IA3Linear(
              layer.in_features, layer.out_features, merge_weights=False
          )
      elif "lora" in efficient_finetune:
          return LoRALinear(
              layer.in_features,
              layer.out_features,
              r=lora_r,
              lora_alpha=lora_alpha,
              merge_weights=False,
          )
      elif (
          efficient_finetune is not None
          and efficient_finetune != "None"
      ):
          raise NotImplementedError(
              f"The efficient finetuning strategy '{efficient_finetune}'"
              f" is not supported. We only support"
              f" {', '.join(PEFT_ADDITIVE_STRATEGIES)}."
          )
  
imports: import logging
  import re
  import warnings
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch._dynamo
  import torch.nn.functional
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from transformers import AutoConfig, AutoModel, AutoTokenizer, BertTokenizer, CLIPTokenizer, ElectraTokenizer
  from transformers.models.mask2former.modeling_mask2former import Mask2FormerLoss
  from ..constants import AUTOMM, CLASS_LOGITS, COLUMN_FEATURES, FEATURES, LOGITS, MASKS, OCR, PEFT_ADDITIVE_STRATEGIES, REGRESSION, SEMANTIC_MASK, SEMANTIC_SEGMENTATION
  from .adaptation_layers import IA3Linear, IA3LoRALinear, LoRALinear
label_desc: Use image node_id 9 for calling a global function with example usage: create_adaptation(efficient_finetune, layer, lora_r, lora_alpha) and returns: IA3LoRALinear, IA3Linear, LoRALinear