code: def __init__(self, d_model: int, max_len: int = 5000):
      super().__init__()
  
      position = torch.arange(max_len).unsqueeze(1)
      div_term = torch.exp(
          torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
      )
      pe = torch.zeros(max_len, 1, d_model)
      pe[:, 0, 0::2] = torch.sin(position * div_term)
      pe[:, 0, 1::2] = torch.cos(position * div_term)
      self.register_buffer("pe", pe)
  
imports: import math
  import torch
  import torch.nn.functional
  from open_alpha_tensor.core.modules.attention import AlphaMultiHeadAttention
label_desc: Use image node_id 1 to create a new PositionEncoding object from inherited base classes: torch.nn with example: obj = PositionEncoding(d_model, max_len)