docstr: Args:
      x: Tensor, with size [seq_len, batch_size, embedding_dim]
docstr_tok: ["Args",":","x",":","Tensor",",","with","size","[","seq_len",",","batch_size",",","embedding_dim","]"]
code: def forward(self, x):
      """
      Args:
          x: Tensor, with size [seq_len, batch_size, embedding_dim]
      """
      x = x + self.pe[: x.size(0)]
      return self.dropout(x)
  
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 2 for calling the PositionalEncoding obj's underlying member method code with example usage: obj.forward(x) and returns: self