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.
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code: def enable_adapters(
      self,
      adapter_setup: AdapterCompositionBlock,
      unfreeze_adapters: bool,
      unfreeze_fusion: bool,
  ):
      if unfreeze_adapters:
          for prefix_tuning_name in adapter_setup.flatten():
              self.pool.enable_prefix(prefix_tuning_name)
              if prefix_tuning_name in self.prefix_gates:
                  for param in self.prefix_gates[
                      prefix_tuning_name
                  ].parameters():
                      param.requires_grad = unfreeze_adapters
  
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 8 for calling the PrefixTuningLayer obj's underlying member method code with example usage: obj.enable_adapters(adapter_setup, unfreeze_adapters, unfreeze_fusion) without return types