code: def _make_weighting_blocks(
      self,
      num_blocks,
      reduce_ratio,
      stride=1,
      freeze_norm=False,
      norm_decay=0.0,
  ):
      layers = []
      for i in range(num_blocks):
          layers.append(
              ConditionalChannelWeightingBlock(
                  self.in_channels,
                  stride=stride,
                  reduce_ratio=reduce_ratio,
                  norm_type=self.norm_type,
                  freeze_norm=freeze_norm,
                  norm_decay=norm_decay,
              )
          )
      return nn.Sequential(*layers)
  
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 LiteHRNetModule obj's underlying member method code with example usage: obj._make_weighting_blocks(num_blocks, reduce_ratio, stride, freeze_norm, norm_decay) and returns: nn