code: def __init__(
      self,
      indices,
      in_chs,
      mid_chs,
      out_ch,
      n_classes,
      resize_mode,
      use_gap,
      align_corners=False,
  ):
      super().__init__()
  
      self.indices = indices[-2::-1]
      self.in_chs = [in_chs[i] for i in indices[::-1]]
      self.mid_chs = mid_chs[::-1]
      self.convs = nn.LayerList()
      self.conv_ups = nn.LayerList()
      for in_ch, mid_ch in zip(self.in_chs[1:], self.mid_chs):
          self.convs.append(
              nn.Conv2D(in_ch, mid_ch, kernel_size=1, bias_attr=False)
          )
          self.conv_ups.append(
              layers.ConvBNReLU(out_ch + mid_ch, out_ch, 1)
          )
      self.conv_w = nn.Sequential(
          nn.AvgPool2D(kernel_size=(49, 49), stride=(16, 20))
          if not use_gap
          else nn.AdaptiveAvgPool2D(1),
          nn.Conv2D(self.in_chs[0], out_ch, 1, bias_attr=False),
          nn.Sigmoid(),
      )
      self.conv_v = layers.ConvBNReLU(self.in_chs[0], out_ch, 1)
      self.conv_t = nn.Conv2D(
          out_ch, out_ch, kernel_size=1, bias_attr=False
      )
      self.conv_out = nn.Conv2D(
          out_ch, n_classes, kernel_size=1, bias_attr=False
      )
  
      self.interp = partial(
          F.interpolate, mode=resize_mode, align_corners=align_corners
      )
  
file_path: PaddleSeg/paddleseg/models/lraspp.py
imports: from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new LRASPPHead object from inherited base classes: nn with example: obj = LRASPPHead(indices, in_chs, mid_chs, out_ch, n_classes, resize_mode, use_gap, align_corners)