code: def __init__(
      self,
      backbone: nn.Module,
      train_backbone: bool,
      num_channels: int,
      return_interm_layers: bool,
  ):
      super().__init__()
      for name, parameter in backbone.named_parameters():
          if (
              not train_backbone
              or "layer2" not in name
              and "layer3" not in name
              and "layer4" not in name
          ):
              parameter.requires_grad_(False)
      if return_interm_layers:
          return_layers = {
              "layer1": "0",
              "layer2": "1",
              "layer3": "2",
              "layer4": "3",
          }
      else:
          return_layers = {"layer4": 0}
      self.body = IntermediateLayerGetter(
          backbone, return_layers=return_layers
      )
      self.num_channels = num_channels
  
imports: import math
  from collections import OrderedDict
  import torch
  import torch.nn.functional
  import torchvision
  from mmf.models.unit.misc import NestedTensor
  from torch import nn
  from torchvision.models._utils import IntermediateLayerGetter
  from torchvision.ops.misc import FrozenBatchNorm2d
label_desc: Use image node_id 1 to create a new BackboneBase object from inherited base classes: nn with example: obj = BackboneBase(backbone, train_backbone, num_channels, return_interm_layers)