code: def __init__(self, config):
      super().__init__()
      self.prefix_projection = config.prefix_projection
      token_dim = config.token_dim
      num_layers = config.num_layers
      encoder_hidden_size = config.encoder_hidden_size
      num_virtual_tokens = config.num_virtual_tokens
      if self.prefix_projection and not config.inference_mode:
          # Use a two-layer MLP to encode the prefix
          self.embedding = torch.nn.Embedding(
              num_virtual_tokens, token_dim
          )
          self.transform = torch.nn.Sequential(
              torch.nn.Linear(token_dim, encoder_hidden_size),
              torch.nn.Tanh(),
              torch.nn.Linear(
                  encoder_hidden_size, num_layers * 2 * token_dim
              ),
          )
      else:
          self.embedding = torch.nn.Embedding(
              num_virtual_tokens, num_layers * 2 * token_dim
          )
  
imports: import torch
label_desc: Use image node_id 1 to create a new PrefixEncoder object from inherited base classes: torch.nn with example: obj = PrefixEncoder(config)