class_docstr: A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
  more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT
  library: https://huggingface.co/docs/peft/index
  
  Currently supported PEFT methods are all non-prefix tuning methods. Below is the list of supported PEFT methods
  that anyone can load, train and run with this mixin class:
  - Low Rank Adapters (LoRA): https://huggingface.co/docs/peft/conceptual_guides/lora
  - IA3: https://huggingface.co/docs/peft/conceptual_guides/ia3
  - AdaLora: https://arxiv.org/abs/2303.10512
  
  Other PEFT models such as prompt tuning, prompt learning are out of scope as these adapters are not "injectable"
  into a torch module. For using these methods, please refer to the usage guide of PEFT library.
  
  With this mixin, if the correct PEFT version is installed, it is possible to:
  
  - Load an adapter stored on a local path or in a remote Hub repository, and inject it in the model
  - Attach new adapters in the model and train them with Trainer or by your own.
  - Attach multiple adapters and iteratively activate / deactivate them
  - Activate / deactivate all adapters from the model.
  - Get the `state_dict` of the active adapter.
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code: def active_adapter(self) -> str:
      warnings.warn(
          "The `active_adapter` method is deprecated and will be removed in a future version.",
          FutureWarning,
      )
  
      return self.active_adapters()[0]
  
file_path: transformers/src/transformers/integrations/peft.py
imports: import inspect
  import warnings
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  from ..utils import check_peft_version, find_adapter_config_file, is_accelerate_available, is_peft_available, is_torch_available, logging
label_desc: Use image node_id 7 for calling the PeftAdapterMixin obj's underlying member method code with example usage: obj.active_adapter() and returns: self