class_docstr: Abstract base class for network backbones.
class_docstr_tok: ["Abstract","base","class","for","network","backbones","."]
docstr: Some backbones require the input height and width to be divisible by a
  specific integer. This is typically true for encoder / decoder type networks
  with lateral connection (e.g., FPN) for which feature maps need to match
  dimension in the "bottom up" and "top down" paths. Set to 0 if no specific
  input size divisibility is required.
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code: def size_divisibility(self) -> int:
      """
      Some backbones require the input height and width to be divisible by a
      specific integer. This is typically true for encoder / decoder type networks
      with lateral connection (e.g., FPN) for which feature maps need to match
      dimension in the "bottom up" and "top down" paths. Set to 0 if no specific
      input size divisibility is required.
      """
      return 0
  
file_path: detectron2/detectron2/modeling/backbone/backbone.py
imports: from abc import ABCMeta, abstractmethod
  from typing import Dict
  import torch.nn
  from detectron2.layers import ShapeSpec
label_desc: Use image node_id 3 for calling the Backbone obj's underlying member method code with example usage: obj.size_divisibility() and returns: int