code: def __init__(
      self,
      in_channels=512,
      hidden_size=256,
      batch_max_legnth=25,
      out_channels=38,
      use_cnt=True,
      use_seq=True,
      **kwargs,
  ):
      super(RFLHead, self).__init__()
      assert use_cnt or use_seq
      self.use_cnt = use_cnt
      self.use_seq = use_seq
      if self.use_cnt:
          self.cnt_head = CNTHead(
              embed_size=in_channels,
              encode_length=batch_max_legnth + 1,
              out_channels=out_channels,
              **kwargs,
          )
      if self.use_seq:
          self.seq_head = AttentionLSTM(
              in_channels=in_channels,
              out_channels=out_channels,
              hidden_size=hidden_size,
              **kwargs,
          )
      self.batch_max_legnth = batch_max_legnth
      self.num_class = out_channels
      self.apply(self.init_weights)
  
file_path: PaddleOCR/ppocr/modeling/heads/rec_rfl_head.py
imports: import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal, KaimingNormal
  from .rec_att_head import AttentionLSTM
label_desc: Use image node_id 1 to create a new RFLHead object from inherited base classes: nn with example: obj = RFLHead(in_channels, hidden_size, batch_max_legnth, out_channels, use_cnt, use_seq)