class_docstr: Representation of a Prefix Tuning layer within one Transformer layer. This class implements `AdapterLayerBase` for
  compatibility with adapters. It uses `PrefixTuningPool` in the background and `set_pool()` must be called after
  initialization.
  
  Args:
      location_key (str): The id describing the location of this layer in the model.
                          Currently, can be "encoder_prefix", "cross_prefix" or None.
      config (:class:`~transformers.PretrainedConfig`): The model config.
class_docstr_tok: ["Representation","of","a","Prefix","Tuning","layer","within","one","Transformer","layer",".","This","class","implements","`","AdapterLayerBase","`","for","compatibility","with","adapters",".","It","uses","`","PrefixTuningPool","`","in","the","background","and","`","set_pool","(",")","`","must","be","called","after","initialization",".","Args",":","location_key","(","str",")",":","The","id","describing","the","location","of","this","layer","in","the","model",".","Currently",",","can","be","``","encoder_prefix","''",",","``","cross_prefix","''","or","None",".","config","(",":","class",":","`","~transformers.PretrainedConfig","`",")",":","The","model","config","."]
code: def get_adapter(self, adapter_name):
      return_dict = nn.ModuleDict()
      # Make sure to only return params once
      if (
          adapter_name in self.prefixes
          and self.prefixes[adapter_name] == 0
      ):
          prefix_module = self.pool.get_prefix(adapter_name)
          if prefix_module is not None:
              return_dict["prefix"] = prefix_module[self.location_key]
      if adapter_name in self.prefix_gates:
          return_dict["gate"] = self.prefix_gates[adapter_name]
      if len(return_dict) > 0:
          return return_dict
  
      return None
  
imports: from typing import Dict, List, NamedTuple, Optional, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from transformers import PretrainedConfig
  from transformers.modeling_utils import ModuleUtilsMixin
  from ..composition import AdapterCompositionBlock, BatchSplit, Parallel, Stack, adjust_tensors_for_parallel
  from ..configuration import ModelAdaptersConfig, PrefixTuningConfig
  from ..context import AdapterSetup, ForwardContext
  from .adapter_layer_base import ComposableAdapterLayerBase
  from .modeling import Activation_Function_Class
label_desc: Use image node_id 10 for calling the PrefixTuningLayer obj's underlying member method code with example usage: obj.get_adapter(adapter_name) and returns: None, return_dict