code: def __init__(self, in_channels=96, reduce_factor=4):
      super(IntraCLBlock, self).__init__()
      self.channels = in_channels
      self.rf = reduce_factor
      weight_attr = paddle.nn.initializer.KaimingUniform()
      self.conv1x1_reduce_channel = nn.Conv2D(
          self.channels,
          self.channels // self.rf,
          kernel_size=1,
          stride=1,
          padding=0,
      )
      self.conv1x1_return_channel = nn.Conv2D(
          self.channels // self.rf,
          self.channels,
          kernel_size=1,
          stride=1,
          padding=0,
      )
  
      self.v_layer_7x1 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(7, 1),
          stride=(1, 1),
          padding=(3, 0),
      )
      self.v_layer_5x1 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(5, 1),
          stride=(1, 1),
          padding=(2, 0),
      )
      self.v_layer_3x1 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(3, 1),
          stride=(1, 1),
          padding=(1, 0),
      )
  
      self.q_layer_1x7 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(1, 7),
          stride=(1, 1),
          padding=(0, 3),
      )
      self.q_layer_1x5 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(1, 5),
          stride=(1, 1),
          padding=(0, 2),
      )
      self.q_layer_1x3 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(1, 3),
          stride=(1, 1),
          padding=(0, 1),
      )
  
      # base
      self.c_layer_7x7 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(7, 7),
          stride=(1, 1),
          padding=(3, 3),
      )
      self.c_layer_5x5 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(5, 5),
          stride=(1, 1),
          padding=(2, 2),
      )
      self.c_layer_3x3 = nn.Conv2D(
          self.channels // self.rf,
          self.channels // self.rf,
          kernel_size=(3, 3),
          stride=(1, 1),
          padding=(1, 1),
      )
  
      self.bn = nn.BatchNorm2D(self.channels)
      self.relu = nn.ReLU()
  
imports: import paddle
  from paddle import nn
label_desc: Use image node_id 1 to create a new IntraCLBlock object from inherited base classes: nn with example: obj = IntraCLBlock(in_channels, reduce_factor)