code: def __init__(
      self, d_model: int, dropout: float = 0.1, max_len: int = 5000
  ):
      super().__init__()
      self.dropout = nn.Dropout(p=dropout)
  
      position = torch.arange(max_len).unsqueeze(1)
      div_term = torch.exp(
          torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
      )
      pe = torch.zeros(max_len, 1, d_model)
      pe[:, 0, 0::2] = torch.sin(position * div_term)
      pe[:, 0, 1::2] = torch.cos(position * div_term)
      self.register_buffer("pe", pe)
  
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 PositionalEncoding object from inherited base classes: nn with example: obj = PositionalEncoding(d_model, dropout, max_len)