code: def __init__(self, in_channels=1, alphabet="0123456789"):
      super(Transformer, self).__init__()
      self.alphabet = alphabet
      word_n_class = self.get_alphabet_len()
      self.embedding_word_with_upperword = Embeddings(512, word_n_class)
      self.pe = PositionalEncoding(dim=512, dropout=0.1, max_len=5000)
  
      self.encoder = Encoder()
      self.decoder = Decoder()
      self.generator_word_with_upperword = Generator(1024, word_n_class)
  
      for p in self.parameters():
          if p.dim() > 1:
              nn.initializer.XavierNormal(p)
  
imports: import copy
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(in_channels, alphabet)