code: def __init__(
      self,
      d_model=512,
      nhead=8,
      num_layers=4,
      dim_feedforward=2048,
      dropout=0.0,
      max_length=25,
      detach=True,
      num_classes=37,
  ):
      super().__init__()
  
      self.d_model = d_model
      self.detach = detach
      self.max_length = max_length + 1  # additional stop token
      self.proj = nn.Linear(num_classes, d_model, bias_attr=False)
      self.token_encoder = PositionalEncoding(
          dropout=0.1, dim=d_model, max_len=self.max_length
      )
      self.pos_encoder = PositionalEncoding(
          dropout=0, dim=d_model, max_len=self.max_length
      )
  
      self.decoder = nn.LayerList(
          [
              TransformerBlock(
                  d_model=d_model,
                  nhead=nhead,
                  dim_feedforward=dim_feedforward,
                  attention_dropout_rate=dropout,
                  residual_dropout_rate=dropout,
                  with_self_attn=False,
                  with_cross_attn=True,
              )
              for i in range(num_layers)
          ]
      )
  
      self.cls = nn.Linear(d_model, num_classes)
  
imports: import math
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle.nn import LayerList
  from ppocr.modeling.heads.rec_nrtr_head import TransformerBlock, PositionalEncoding
label_desc: Use image node_id 1 to create a new BCNLanguage object from inherited base classes: nn with example: obj = BCNLanguage(d_model, nhead, num_layers, dim_feedforward, dropout, max_length, detach, num_classes)