code: def _make_fuse_layers(self, freeze_norm=False, norm_decay=0.0):
      if self.num_branches == 1:
          return None
      fuse_layers = []
      num_out_branches = (
          self.num_branches if self.multiscale_output else 1
      )
      for i in range(num_out_branches):
          fuse_layer = []
          for j in range(self.num_branches):
              if j > i:
                  fuse_layer.append(
                      nn.Sequential(
                          L.Conv2d(
                              self.in_channels[j],
                              self.in_channels[i],
                              kernel_size=1,
                              stride=1,
                              padding=0,
                              bias=False,
                          ),
                          nn.BatchNorm2D(self.in_channels[i]),
                          nn.Upsample(
                              scale_factor=2 ** (j - i), mode="nearest"
                          ),
                      )
                  )
              elif j == i:
                  fuse_layer.append(None)
              else:
                  conv_downsamples = []
                  for k in range(i - j):
                      if k == i - j - 1:
                          conv_downsamples.append(
                              nn.Sequential(
                                  L.Conv2d(
                                      self.in_channels[j],
                                      self.in_channels[j],
                                      kernel_size=3,
                                      stride=2,
                                      padding=1,
                                      groups=self.in_channels[j],
                                      bias=False,
                                  ),
                                  nn.BatchNorm2D(self.in_channels[j]),
                                  L.Conv2d(
                                      self.in_channels[j],
                                      self.in_channels[i],
                                      kernel_size=1,
                                      stride=1,
                                      padding=0,
                                      bias=False,
                                  ),
                                  nn.BatchNorm2D(self.in_channels[i]),
                              )
                          )
                      else:
                          conv_downsamples.append(
                              nn.Sequential(
                                  L.Conv2d(
                                      self.in_channels[j],
                                      self.in_channels[j],
                                      kernel_size=3,
                                      stride=2,
                                      padding=1,
                                      groups=self.in_channels[j],
                                      bias=False,
                                  ),
                                  nn.BatchNorm2D(self.in_channels[j]),
                                  L.Conv2d(
                                      self.in_channels[j],
                                      self.in_channels[j],
                                      kernel_size=1,
                                      stride=1,
                                      padding=0,
                                      bias=False,
                                  ),
                                  nn.BatchNorm2D(self.in_channels[j]),
                                  nn.ReLU(),
                              )
                          )
  
                  fuse_layer.append(nn.Sequential(*conv_downsamples))
          fuse_layers.append(nn.LayerList(fuse_layer))
  
      return nn.LayerList(fuse_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 4 for calling the LiteHRNetModule obj's underlying member method code with example usage: obj._make_fuse_layers(freeze_norm, norm_decay) and returns: nn, None