code: def forward(self, x):
      mini_size = x[-1].shape[-2:]
      out = [F.adaptive_avg_pool2d(s, mini_size) for s in x[:-1]] + [
          x[-1]
      ]
      out = paddle.concat(out, 1)
      out = self.conv1(out)
      out = self.conv2(out)
      out = paddle.split(out, self.channels, 1)
      out = [
          s * F.interpolate(a, s.shape[-2:], mode="nearest")
          for s, a in zip(x, out)
      ]
      return out
  
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 2 for calling the CrossResolutionWeightingModule obj's underlying member method code with example usage: obj.forward(x) and returns: out