class_docstr: Learns a position embedding
class_docstr_tok: ["Learns","a","position","embedding"]
code: def __init__(self, d_model=256, max_len=512):
      super().__init__()
      self.timing_table = nn.Parameter(
          torch.FloatTensor(max_len, d_model)
      )
      nn.init.normal_(self.timing_table)
  
imports: import copy
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from stanza.models.constituency.positional_encoding import ConcatSinusoidalEncoding
label_desc: Use image node_id 1 to create a new ConcatPositionalEncoding object from inherited base classes: nn with example: obj = ConcatPositionalEncoding(d_model, max_len)