code: def __init__(
      self,
      in_channels=64,
      out_channels=64,
      kernel_size=3,
      stride=1,
      padding=1,
      bias=True,
      mode="CRC",
      negative_slope=0.2,
  ):
      super(QFAttention, self).__init__()
  
      assert (
          in_channels == out_channels
      ), "Only support in_channels==out_channels."
      if mode[0] in ["R", "L"]:
          mode = mode[0].lower() + mode[1:]
  
      self.res = conv(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          bias,
          mode,
          negative_slope,
      )
  
imports: from collections import OrderedDict
  import numpy
  import paddle.nn
label_desc: Use image node_id 1 to create a new QFAttention object from inherited base classes: nn with example: obj = QFAttention(in_channels, out_channels, kernel_size, stride, padding, bias, mode, negative_slope)