class_docstr: ResNetDilatedBottleneck as used in 'Deep Attentive Features for Prostate Segmentation in 3D
  Transrectal Ultrasound' <https://arxiv.org/pdf/1907.01743.pdf>.
  Same as Daf3dResNetBottleneck but dilation of 2 is used in second convolution.
  Args:
      in_planes: number of input channels.
      planes: number of output channels (taking expansion into account).
      spatial_dims: number of spatial dimensions of the input image.
      stride: stride to use for second conv layer.
      downsample: which downsample layer to use.
class_docstr_tok: ["ResNetDilatedBottleneck","as","used","in","'Deep","Attentive","Features","for","Prostate","Segmentation","in","3D","Transrectal","Ultrasound","'","<","https",":","\/\/arxiv.org\/pdf\/1907.01743.pdf",">",".","Same","as","Daf3dResNetBottleneck","but","dilation","of","2","is","used","in","second","convolution",".","Args",":","in_planes",":","number","of","input","channels",".","planes",":","number","of","output","channels","(","taking","expansion","into","account",")",".","spatial_dims",":","number","of","spatial","dimensions","of","the","input","image",".","stride",":","stride","to","use","for","second","conv","layer",".","downsample",":","which","downsample","layer","to","use","."]
code: def __init__(
      self, in_planes, planes, spatial_dims=3, stride=1, downsample=None
  ):
      super().__init__(
          in_planes, planes, spatial_dims, stride, downsample
      )
  
      # add dilation in second convolution
      conv_type: Callable = Conv[Conv.CONV, spatial_dims]
      self.conv2 = conv_type(
          planes,
          planes,
          kernel_size=3,
          stride=stride,
          padding=2,
          dilation=2,
          groups=32,
          bias=False,
      )
  
imports: from __future__ import annotations
  from collections import OrderedDict
  from collections.abc import Callable, Sequence
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from monai.networks.blocks import ADN
  from monai.networks.blocks.aspp import SimpleASPP
  from monai.networks.blocks.backbone_fpn_utils import BackboneWithFPN
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.blocks.feature_pyramid_network import ExtraFPNBlock, FeaturePyramidNetwork
  from monai.networks.layers.factories import Conv, Norm
  from monai.networks.nets.resnet import ResNet, ResNetBottleneck
label_desc: Use image node_id 1 to create a new Daf3dResNetDilatedBottleneck object from inherited base classes: Daf3dResNetBottleneck with example: obj = Daf3dResNetDilatedBottleneck(in_planes, planes, spatial_dims, stride, downsample)