code: def __init__(self, d_model, max_len=1000):
      super(PositionalEncoding, self).__init__()
      pe = torch.zeros(max_len, d_model)
      position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(
          1
      )
      div_term = torch.exp(
          torch.arange(0, d_model, 2).float()
          * (-math.log(10000.0) / d_model)
      )
      pe[:, 0::2] = torch.sin(position * div_term)
      pe[:, 1::2] = torch.cos(position * div_term)
      pe = pe.unsqueeze(0).transpose(0, 1)
      self.register_buffer("pe", pe)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  import math
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 1 to create a new PositionalEncoding object from inherited base classes: nn with example: obj = PositionalEncoding(d_model, max_len)