code: def __init__(self, hidden_dim, max_seq_len=80):
      super().__init__()
      self.hidden_dim = hidden_dim
  
      pe = torch.zeros(max_seq_len, hidden_dim)
      for pos in range(max_seq_len):
          for i in range(0, hidden_dim, 2):
              pe[pos, i] = math.sin(
                  pos / (10000 ** ((2 * i) / hidden_dim))
              )
              pe[pos, i + 1] = math.cos(
                  pos / (10000 ** ((2 * (i + 1)) / hidden_dim))
              )
  
      pe = pe.unsqueeze(0)
      self.register_buffer("pe", pe)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import math
  import copy
label_desc: Use image node_id 1 to create a new PosEncoding object from inherited base classes: nn with example: obj = PosEncoding(hidden_dim, max_seq_len)