code: def __init__(
      self,
      in_channels,
      out_channels,
      d_model=512,
      nhead=8,
      num_layers=3,
      dim_feedforward=2048,
      dropout=0.1,
      max_length=25,
      use_lang=False,
      iter_size=1,
  ):
      super().__init__()
      self.max_length = max_length + 1
      self.pos_encoder = PositionalEncoding(
          dropout=0.1, dim=d_model, max_len=8 * 32
      )
      self.encoder = nn.LayerList(
          [
              TransformerBlock(
                  d_model=d_model,
                  nhead=nhead,
                  dim_feedforward=dim_feedforward,
                  attention_dropout_rate=dropout,
                  residual_dropout_rate=dropout,
                  with_self_attn=True,
                  with_cross_attn=False,
              )
              for i in range(num_layers)
          ]
      )
      self.decoder = PositionAttention(
          max_length=max_length + 1,  # additional stop token
          mode="nearest",
      )
      self.out_channels = out_channels
      self.cls = nn.Linear(d_model, self.out_channels)
      self.use_lang = use_lang
      if use_lang:
          self.iter_size = iter_size
          self.language = BCNLanguage(
              d_model=d_model,
              nhead=nhead,
              num_layers=4,
              dim_feedforward=dim_feedforward,
              dropout=dropout,
              max_length=max_length,
              num_classes=self.out_channels,
          )
          # alignment
          self.w_att_align = nn.Linear(2 * d_model, d_model)
          self.cls_align = nn.Linear(d_model, self.out_channels)
  
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 ABINetHead object from inherited base classes: nn with example: obj = ABINetHead(in_channels, out_channels, d_model, nhead, num_layers, dim_feedforward, dropout, max_length, use_lang, iter_size)