code: def __init__(
      self,
      n_token: int,
      n_head: int = 8,
      d_model: int = 512,
      d_ff: int = 2048,
  ):
      super().__init__()
      p_dropout = nn.ValueChoice(
          [0.1, 0.2, 0.3, 0.4, 0.5], label="p_dropout"
      )
      n_layer = nn.ValueChoice([5, 6, 7, 8, 9], label="n_layer")
      self.encoder = nn.TransformerEncoder(
          nn.TransformerEncoderLayer(d_model, n_head, d_ff, p_dropout),
          n_layer,
      )
      self.d_model = d_model
      self.decoder = nn.Linear(d_model, n_token)
      self.embeddings = nn.Embedding(n_token, d_model)
      self.position = PositionalEncoding(d_model)
  
imports: import torch
  import torch.nn.functional
  import nni.retiarii.nn.pytorch
  from nni.retiarii import model_wrapper
  import nni
  import nni.retiarii.strategy
  from nni.retiarii.evaluator import FunctionalEvaluator
  from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
  import math
  from torchtext.datasets import WikiText2
  from torchtext.data.utils import get_tokenizer
  from torchtext.vocab import build_vocab_from_iterator
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(n_token, n_head, d_model, d_ff)