class_docstr: FastSCNN feature fusion module
class_docstr_tok: ["FastSCNN","feature","fusion","module"]
code: def __init__(
      self,
      highter_in_channels,
      lower_in_channels,
      out_channels,
      height,
      width,
      scale_factor=4,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(FeatureFusionModule, self).__init__()
      self.scale_factor = scale_factor
      self._up_kwargs = {"height": height, "width": width}
  
      with self.name_scope():
          self.dwconv = _DWConv(
              lower_in_channels,
              out_channels,
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
          )
          self.conv_lower_res = nn.Conv2D(
              in_channels=out_channels,
              channels=out_channels,
              kernel_size=1,
          )
          self.conv_higher_res = nn.Conv2D(
              in_channels=highter_in_channels,
              channels=out_channels,
              kernel_size=1,
          )
          self.bn = norm_layer(in_channels=out_channels)
          self.bn_high = norm_layer(in_channels=out_channels)
          self.relu = nn.Activation("relu")
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new FeatureFusionModule object from inherited base classes: HybridBlock with example: obj = FeatureFusionModule(highter_in_channels, lower_in_channels, out_channels, height, width, scale_factor, norm_layer, norm_kwargs)