code: def forward(self, x):
      # x dimension: (L, B, E)
      # batch_size, seq_len, d_model
      seq_len = x.shape[0]
      pos_encoding = self.pe[:seq_len, :]
      x = x + pos_encoding
      return self.dropout(x)
  
imports: import logging
  import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import parameters, types
  from reagent.models import convolutional_network, fully_connected_network
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import ACTIVATION_MAP
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