class_docstr: ResNetV1d variant described in [1]_.
  
  Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in
  the input stem with three 3x3 convs. And in the downsampling block, a 2x2
  avg_pool with stride 2 is added before conv, whose stride is changed to 1.
class_docstr_tok: ["ResNetV1d","variant","described","in","[","1","]","_",".","Compared","with","default","ResNet","(","ResNetV1b",")",",","ResNetV1d","replaces","the","7x7","conv","in","the","input","stem","with","three","3x3","convs",".","And","in","the","downsampling","block",",","a","2x2","avg_pool","with","stride","2","is","added","before","conv",",","whose","stride","is","changed","to","1","."]
code: def __init__(self, **kwargs):
      super(ResNetV1d, self).__init__(
          deep_stem=True, avg_down=True, **kwargs
      )
  
imports: import torch.nn
  import torch.utils.checkpoint
  from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer, constant_init, kaiming_init
  from annotator.uniformer.mmcv.runner import load_checkpoint
  from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
  from annotator.uniformer.mmseg.utils import get_root_logger
  from ..builder import BACKBONES
  from ..utils import ResLayer
label_desc: Use image node_id 1 to create a new ResNetV1d object from inherited base classes: ResNet with example: obj = ResNetV1d()