code: def __init__(self, module_configs, prefix_tuning_config):
      super().__init__()
      if prefix_tuning_config["flat"]:
          prefix_tuning_class = FlatPrefixTuning
      else:
          prefix_tuning_class = PrefixTuning
      for k, kwargs in module_configs.items():
          self[k] = prefix_tuning_class(
              **kwargs, config=prefix_tuning_config
          )
  
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 1 to create a new PrefixTuningGroup object from inherited base classes: nn with example: obj = PrefixTuningGroup(module_configs, prefix_tuning_config)