code: def __init__(self, d_model, max_seq_len=160):
      super().__init__()
      assert (
          d_model % 2 == 0
      ), "model dimension has to be multiple of 2 (encode sin(pos) and cos(pos))"
      self.d_model = d_model
      pe = torch.zeros(max_seq_len, d_model)
      for pos in range(max_seq_len):
          for i in range(0, d_model, 2):
              pe[pos, i] = math.sin(
                  pos / (10000 ** ((2 * i) / d_model))
              )
              pe[pos, i + 1] = math.cos(
                  pos / (10000 ** ((2 * (i + 1)) / d_model))
              )
      pe = pe.unsqueeze(0)
      self.register_buffer("pe", pe)
  
imports: import math
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new PositionalEncoder object from inherited base classes: torch.nn with example: obj = PositionalEncoder(d_model, max_seq_len)