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(
                          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(
                                  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]),
                                  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(
                                  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]),
                                  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 paddleseg.cvlibs import manager
  from paddleseg import utils
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