code: def __init__(self, inplanes=64, embed_dim=384):
      super().__init__()
  
      self.stem = nn.Sequential(
          *[
              nn.Conv2D(
                  3,
                  inplanes,
                  kernel_size=3,
                  stride=2,
                  padding=1,
                  bias_attr=False,
              ),
              nn.SyncBatchNorm(inplanes),
              nn.ReLU(),
              nn.Conv2D(
                  inplanes,
                  inplanes,
                  kernel_size=3,
                  stride=1,
                  padding=1,
                  bias_attr=False,
              ),
              nn.SyncBatchNorm(inplanes),
              nn.ReLU(),
              nn.Conv2D(
                  inplanes,
                  inplanes,
                  kernel_size=3,
                  stride=1,
                  padding=1,
                  bias_attr=False,
              ),
              nn.SyncBatchNorm(inplanes),
              nn.ReLU(),
              nn.MaxPool2D(kernel_size=3, stride=2, padding=1),
          ]
      )
      self.conv2 = nn.Sequential(
          *[
              nn.Conv2D(
                  inplanes,
                  2 * inplanes,
                  kernel_size=3,
                  stride=2,
                  padding=1,
                  bias_attr=False,
              ),
              nn.SyncBatchNorm(2 * inplanes),
              nn.ReLU(),
          ]
      )
      self.conv3 = nn.Sequential(
          *[
              nn.Conv2D(
                  2 * inplanes,
                  4 * inplanes,
                  kernel_size=3,
                  stride=2,
                  padding=1,
                  bias_attr=False,
              ),
              nn.SyncBatchNorm(4 * inplanes),
              nn.ReLU(),
          ]
      )
      self.conv4 = nn.Sequential(
          *[
              nn.Conv2D(
                  4 * inplanes,
                  4 * inplanes,
                  kernel_size=3,
                  stride=2,
                  padding=1,
                  bias_attr=False,
              ),
              nn.SyncBatchNorm(4 * inplanes),
              nn.ReLU(),
          ]
      )
      self.fc1 = nn.Conv2D(
          inplanes,
          embed_dim,
          kernel_size=1,
          stride=1,
          padding=0,
          bias_attr=True,
      )
      self.fc2 = nn.Conv2D(
          2 * inplanes,
          embed_dim,
          kernel_size=1,
          stride=1,
          padding=0,
          bias_attr=True,
      )
      self.fc3 = nn.Conv2D(
          4 * inplanes,
          embed_dim,
          kernel_size=1,
          stride=1,
          padding=0,
          bias_attr=True,
      )
      self.fc4 = nn.Conv2D(
          4 * inplanes,
          embed_dim,
          kernel_size=1,
          stride=1,
          padding=0,
          bias_attr=True,
      )
  
imports: import math
  import warnings
  from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models.backbones.transformer_utils import DropPath
  from paddleseg.models.layers.ms_deformable_attention import MSDeformAttn
label_desc: Use image node_id 1 to create a new SpatialPriorModule object from inherited base classes: nn with example: obj = SpatialPriorModule(inplanes, embed_dim)