code: def eject(self):
      input_tokens = torch.arange(self.config.prefix_length).long()
      input_tokens = (
          input_tokens.unsqueeze(0).expand(1, -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(
          self.config.prefix_length
          * self.n_layers
          * 2
          * self.input_size
      )  # *2 for key and value
  
      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 PrefixTuning obj's underlying member method code with example usage: obj.eject() and returns: key_values