code: def __init__(
      self,
      n_layers: int,
      n_heads: int,
      input_size: int,
      config: PrefixTuningConfig,
      n_embd_per_head: Optional[int] = None,
  ):
      super().__init__()
      self.n_layers = n_layers
      self.n_heads = n_heads
      self.input_size = input_size
      self.n_embd_per_head = (
          n_embd_per_head or self.input_size // self.n_heads
      )
      self.config = config
  
      self.control_trans = nn.Parameter(
          torch.randn(
              self.config.prefix_length
              * self.n_layers
              * 2
              * self.n_heads
              * self.n_embd_per_head
          )
      )
  
      self.dropout = nn.Dropout(self.config.dropout)
  
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 FlatPrefixTuning object from inherited base classes: nn, ModuleUtilsMixin with example: obj = FlatPrefixTuning(n_layers, n_heads, input_size, config, n_embd_per_head)