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 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 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)