code: def __init__(self, channels):
      super(RecurrentResidualBlock, self).__init__()
      self.conv1 = nn.Conv2D(
          channels, channels, kernel_size=3, padding=1
      )
      self.bn1 = nn.BatchNorm2D(channels)
      self.gru1 = GruBlock(channels, channels)
      # self.prelu = nn.ReLU()
      self.prelu = mish()
      self.conv2 = nn.Conv2D(
          channels, channels, kernel_size=3, padding=1
      )
      self.bn2 = nn.BatchNorm2D(channels)
      self.gru2 = GruBlock(channels, channels)
      self.feature_enhancer = FeatureEnhancer()
  
      for p in self.parameters():
          if p.dim() > 1:
              paddle.nn.initializer.XavierUniform(p)
  
imports: import math
  import warnings
  import numpy
  import paddle
  from paddle import nn
  import string
  from .tps_spatial_transformer import TPSSpatialTransformer
  from .stn import STN
  from .tsrn import GruBlock, mish, UpsampleBLock
  from ppocr.modeling.heads.sr_rensnet_transformer import Transformer, LayerNorm, PositionwiseFeedForward, MultiHeadedAttention
label_desc: Use image node_id 1 to create a new RecurrentResidualBlock object from inherited base classes: nn with example: obj = RecurrentResidualBlock(channels)