code: def __init__(
      self,
      num_branches,
      num_blocks,
      in_channels,
      reduce_ratio,
      module_type,
      multiscale_output=False,
      with_fuse=True,
      norm_type="bn",
      freeze_norm=False,
      norm_decay=0.0,
  ):
      super(LiteHRNetModule, self).__init__()
      assert num_branches == len(
          in_channels
      ), "num_branches {} should equal to num_in_channels {}".format(
          num_branches, len(in_channels)
      )
      assert module_type in [
          "LITE",
          "NAIVE",
      ], "module_type should be one of ['LITE', 'NAIVE']"
      self.num_branches = num_branches
      self.in_channels = in_channels
      self.multiscale_output = multiscale_output
      self.with_fuse = with_fuse
      self.norm_type = "bn"
      self.module_type = module_type
  
      if self.module_type == "LITE":
          self.layers = self._make_weighting_blocks(
              num_blocks,
              reduce_ratio,
              freeze_norm=freeze_norm,
              norm_decay=norm_decay,
          )
      elif self.module_type == "NAIVE":
          self.layers = self._make_naive_branches(
              num_branches,
              num_blocks,
              freeze_norm=freeze_norm,
              norm_decay=norm_decay,
          )
  
      if self.with_fuse:
          self.fuse_layers = self._make_fuse_layers(
              freeze_norm=freeze_norm, norm_decay=norm_decay
          )
          self.relu = nn.ReLU()
  
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 1 to create a new LiteHRNetModule object from inherited base classes: nn with example: obj = LiteHRNetModule(num_branches, num_blocks, in_channels, reduce_ratio, module_type, multiscale_output, with_fuse, norm_type, freeze_norm, norm_decay)