code: def __init__(
      self, in_channels: int, out_channels: int, dilation: int
  ) -> None:
      modules = [
          nn.Conv2d(
              in_channels,
              out_channels,
              3,
              padding=dilation,
              dilation=dilation,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(),
      ]
      super().__init__(*modules)
  
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 ASPPConv object from inherited base classes: nn with example: obj = ASPPConv(in_channels, out_channels, dilation)