code: def __init__(
      self,
      in_channels: int,
      num_classes: int,
      atrous_rates: Sequence[int] = (12, 24, 36),
  ) -> None:
      super().__init__(
          ASPP(in_channels, atrous_rates),
          nn.Conv2d(256, 256, 3, padding=1, bias=False),
          nn.BatchNorm2d(256),
          nn.ReLU(),
          nn.Conv2d(256, num_classes, 1),
      )
  
imports: from functools import partial
  from typing import Any, Optional, Sequence
  import torch
  from torch import nn
  from torch.nn import functional
  from ...transforms._presets import SemanticSegmentation
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _VOC_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface, IntermediateLayerGetter
  from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights, MobileNetV3
  from ..resnet import ResNet, resnet101, ResNet101_Weights, resnet50, ResNet50_Weights
  from ._utils import _SimpleSegmentationModel
  from .fcn import FCNHead
label_desc: Use image node_id 1 to create a new DeepLabHead object from inherited base classes: nn with example: obj = DeepLabHead(in_channels, num_classes, atrous_rates)