code: def __init__(
      self,
      channels,
      ratio=16,
      norm_type="bn",
      freeze_norm=False,
      norm_decay=0.0,
  ):
      super(CrossResolutionWeightingModule, self).__init__()
      self.channels = channels
      total_channel = sum(channels)
      self.conv1 = ConvNormLayer(
          ch_in=total_channel,
          ch_out=total_channel // ratio,
          filter_size=1,
          stride=1,
          norm_type=norm_type,
          act="relu",
          freeze_norm=freeze_norm,
          norm_decay=norm_decay,
      )
      self.conv2 = ConvNormLayer(
          ch_in=total_channel // ratio,
          ch_out=total_channel,
          filter_size=1,
          stride=1,
          norm_type=norm_type,
          act="sigmoid",
          freeze_norm=freeze_norm,
          norm_decay=norm_decay,
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from numbers import Integral
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant
  from ppdet.core.workspace import register
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.modeling.ops import channel_shuffle
  from ..None import layers
label_desc: Use image node_id 1 to create a new CrossResolutionWeightingModule object from inherited base classes: nn with example: obj = CrossResolutionWeightingModule(channels, ratio, norm_type, freeze_norm, norm_decay)