class_docstr: Implement the PE function. 
code: def __init__(self, d_model, dropout=0.0, max_len=5000):
      super(PositionalEncoding, self).__init__()
      self.dropout = nn.Dropout(p=dropout)
  
      # Compute the positional encodings once in log space.
      pe = paddle.zeros([max_len, d_model])
      position = (
          paddle.arange(0, max_len).unsqueeze(1).astype("float32")
      )
      div_term = paddle.exp(
          paddle.arange(0, d_model, 2) * -math.log(10000.0) / d_model
      )
      pe[:, 0::2] = paddle.sin(position * div_term)
      pe[:, 1::2] = paddle.cos(position * div_term)
      pe = pe.unsqueeze(0)
      self.register_buffer("pe", pe)
  
imports: import copy
  import math
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 1 to create a new PositionalEncoding object from inherited base classes: nn with example: obj = PositionalEncoding(d_model, dropout, max_len)