class_docstr: The model layer that holds all Prefix Tuning prefixes. While each Transformers layer has its own prefix, this layer
  is shared across all Transformers layers.
  
  How it works:
  
      1. A `PrefixTuningLayer` module that sets this module as pool module is added to each layer.
      2. On adding a prefix, each shim module where a prefix should be added increments a counter in `prefix_counts`.
      3. Finally, the base model class confirms adding a new prefix by calling `confirm_prefix()`.
      4. This module adds a prefix layer that produces outputs corresponding to the indicated number of layers.
  
  Notes:
  
      - The forward call to this layer is executed in the ForwardContext of each model pass.
      - All other methods of this class (except for `confirm_prefix()`) should be called exclusively by
        `PrefixTuningLayer`.
  
  Args:
      config (:class:`~transformers.PretrainedConfig`): The model config.
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code: def enable_prefix(self, prefix_name: str):
      if prefix_name in self.prefix_tunings:
          for param in self.prefix_tunings[prefix_name].parameters():
              param.requires_grad = True
  
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 6 for calling the PrefixTuningPool obj's underlying member method code with example usage: obj.enable_prefix(prefix_name) without return types