code: def forward(self, batch_size):
      key_values = (
          self.control_trans.unsqueeze(0)
          .expand(batch_size, -1)
          .view(
              batch_size,
              self.config.prefix_length,
              self.n_layers * 2,
              self.n_heads,
              self.n_embd_per_head,
          )
          .to(self.device)
      )  # *2 for key and value
      key_values = self.dropout(key_values)
      # n_layers * (2 x batch_size x n_heads x prefix_length x n_embd_per_head)
      key_values = key_values.permute(2, 0, 3, 1, 4).split(2)
  
      return key_values
  
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 2 for calling the FlatPrefixTuning obj's underlying member method code with example usage: obj.forward(batch_size) and returns: key_values