code: def __init__(
      self,
      max_length,
      in_channels=512,
      num_channels=64,
      h=8,
      w=32,
      mode="nearest",
      **kwargs,
  ):
      super().__init__()
      self.max_length = max_length
      self.k_encoder = nn.Sequential(
          encoder_layer(in_channels, num_channels, s=(1, 2)),
          encoder_layer(num_channels, num_channels, s=(2, 2)),
          encoder_layer(num_channels, num_channels, s=(2, 2)),
          encoder_layer(num_channels, num_channels, s=(2, 2)),
      )
      self.k_decoder = nn.Sequential(
          decoder_layer(
              num_channels, num_channels, scale_factor=2, mode=mode
          ),
          decoder_layer(
              num_channels, num_channels, scale_factor=2, mode=mode
          ),
          decoder_layer(
              num_channels, num_channels, scale_factor=2, mode=mode
          ),
          decoder_layer(
              num_channels, in_channels, size=(h, w), mode=mode
          ),
      )
  
      self.pos_encoder = PositionalEncoding(
          dropout=0, dim=in_channels, max_len=max_length
      )
      self.project = nn.Linear(in_channels, in_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 PositionAttention object from inherited base classes: nn with example: obj = PositionAttention(max_length, in_channels, num_channels, h, w, mode)