code: def forward(self, batch_size):
      input_tokens = torch.arange(self.config.prefix_length).long()
      input_tokens = (
          input_tokens.unsqueeze(0)
          .expand(batch_size, -1)
          .to(self.device)
      )
      embs = self.wte(input_tokens)
      key_values = self.control_trans(
          embs
      )  # batch_size x prefix_length x n_layers*2*input_size
      key_values = key_values.view(
          batch_size,
          self.config.prefix_length,
          self.n_layers * 2,
          self.n_heads,
          self.n_embd_per_head,
      )  # *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 3 for calling the PrefixTuning obj's underlying member method code with example usage: obj.forward(batch_size) and returns: key_values