code: def __init__(self, num_classes, n_blocks, pyramids, **kwargs):
      super(DeepLabV2, self).__init__()
      self.add_module(
          "layer1",
          nn.Sequential(
              OrderedDict(
                  [
                      ("conv1", _ConvBatchNormReLU(3, 64, 7, 2, 3, 1)),
                      ("pool", nn.MaxPool2d(3, 2, 1, ceil_mode=True)),
                  ]
              )
          ),
      )
      self.add_module(
          "layer2", _ResBlock(n_blocks[0], 64, 64, 256, 1, 1)
      )
      self.add_module(
          "layer3", _ResBlock(n_blocks[1], 256, 128, 512, 2, 1)
      )
      self.add_module(
          "layer4", _ResBlock(n_blocks[2], 512, 256, 1024, 1, 2)
      )
      self.add_module(
          "layer5", _ResBlock(n_blocks[3], 1024, 512, 2048, 1, 4)
      )
      self.add_module("aspp", _ASPPModule(2048, num_classes, pyramids))
  
file_path: pywick/pywick/models/segmentation/testnets/deeplabv2.py
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from .resnet import _ConvBatchNormReLU, _ResBlock
  from .msc import MSC
label_desc: Use image node_id 1 to create a new DeepLabV2 object from inherited base classes: nn with example: obj = DeepLabV2(num_classes, n_blocks, pyramids)