code: def __init__(self, d_model, max_len=5000):
      super(PositionalEmbedding, self).__init__()
      # Compute the positional encodings once in log space.
      pe = torch.zeros(max_len, d_model).float()
      pe.require_grad = False
  
      position = torch.arange(0, max_len).float().unsqueeze(1)
      div_term = (
          torch.arange(0, d_model, 2).float()
          * -(math.log(10000.0) / d_model)
      ).exp()
  
      pe[:, 0::2] = torch.sin(position * div_term)
      pe[:, 1::2] = torch.cos(position * div_term)
  
      pe = pe.unsqueeze(0)
      self.register_buffer("pe", pe)
  
imports: import math
label_desc: Use image node_id 1 to create a new PositionalEmbedding object from inherited base classes: nn with example: obj = PositionalEmbedding(d_model, max_len)