code: def __init__(
      self,
      num_classes,
      in_channels,
      backbone,
      mlahead_channels=128,
      aux_channels=256,
      norm_layer=nn.BatchNorm2D,
      pretrained=None,
      **kwargs,
  ):
      super(MLATransformer, self).__init__()
  
      self.BatchNorm = norm_layer
      self.mlahead_channels = mlahead_channels
      self.num_classes = num_classes
      self.in_channels = in_channels
      self.backbone = backbone
  
      self.mlahead = MLAHeads(mlahead_channels=self.mlahead_channels)
      self.cls = nn.Conv2D(
          4 * self.mlahead_channels, self.num_classes, 3, padding=1
      )
  
      self.conv0 = layers.ConvBNReLU(
          self.in_channels[0],
          self.in_channels[0] * 2,
          3,
          padding=1,
          bias_attr=False,
      )
      self.conv1 = layers.ConvBNReLU(
          self.in_channels[1],
          self.in_channels[1],
          3,
          padding=1,
          bias_attr=False,
      )
      self.conv21 = layers.ConvBNReLU(
          self.in_channels[2],
          self.in_channels[2],
          3,
          padding=1,
          bias_attr=False,
      )
      self.conv22 = layers.ConvBNReLU(
          self.in_channels[2],
          self.in_channels[2] // 2,
          3,
          padding=1,
          bias_attr=False,
      )
      self.conv31 = layers.ConvBNReLU(
          self.in_channels[3],
          self.in_channels[3],
          3,
          padding=1,
          bias_attr=False,
      )
      self.conv32 = layers.ConvBNReLU(
          self.in_channels[3],
          self.in_channels[3] // 2,
          3,
          padding=1,
          bias_attr=False,
      )
      self.conv33 = layers.ConvBNReLU(
          self.in_channels[3] // 2,
          self.in_channels[3] // 4,
          3,
          padding=1,
          bias_attr=False,
      )
  
      self.aux_head = nn.Sequential(
          layers.ConvBN(
              in_channels=self.in_channels[2],
              out_channels=aux_channels,
              kernel_size=3,
              padding=1,
              bias_attr=False,
          ),
          nn.Conv2D(
              in_channels=aux_channels,
              out_channels=self.num_classes,
              kernel_size=1,
          ),
      )
  
      self.pretrained = pretrained
      self.init_weight()
  
file_path: PaddleSeg/paddleseg/models/mla_transformer.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new MLATransformer object from inherited base classes: nn with example: obj = MLATransformer(num_classes, in_channels, backbone, mlahead_channels, aux_channels, norm_layer, pretrained)