class_docstr: Backbone for 3D Feature Pyramid Network in DAF3D module based on 'Deep Attentive Features for Prostate Segmentation in
  3D Transrectal Ultrasound' <https://arxiv.org/pdf/1907.01743.pdf>.
  
  Args:
      n_input_channels: number of input channels for the first convolution.
class_docstr_tok: ["Backbone","for","3D","Feature","Pyramid","Network","in","DAF3D","module","based","on","'Deep","Attentive","Features","for","Prostate","Segmentation","in","3D","Transrectal","Ultrasound","'","<","https",":","\/\/arxiv.org\/pdf\/1907.01743.pdf",">",".","Args",":","n_input_channels",":","number","of","input","channels","for","the","first","convolution","."]
code: def __init__(self, n_input_channels):
      super().__init__()
      net = Daf3dResNet(
          layers=[3, 4, 6, 3],
          block_inplanes=[128, 256, 512, 1024],
          n_input_channels=n_input_channels,
          num_classes=2,
          bias_downsample=False,
      )
      net_modules = list(net.children())
      self.layer0 = nn.Sequential(*net_modules[:3])
      self.layer1 = nn.Sequential(*net_modules[3:5])
      self.layer2 = net_modules[5]
      self.layer3 = net_modules[6]
      self.layer4 = net_modules[7]
  
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 Daf3dBackbone object from inherited base classes: nn with example: obj = Daf3dBackbone(n_input_channels)