class_docstr: A head with several 3x3 conv layers (each followed by norm & relu) and then
  several fc layers (each followed by relu).
class_docstr_tok: ["A","head","with","several","3x3","conv","layers","(","each","followed","by","norm","&","relu",")","and","then","several","fc","layers","(","each","followed","by","relu",")","."]
docstr: Returns:
      ShapeSpec: the output feature shape
docstr_tok: ["Returns",":","ShapeSpec",":","the","output","feature","shape"]
code: def output_shape(self):
      """
      Returns:
          ShapeSpec: the output feature shape
      """
      o = self._output_size
      if isinstance(o, int):
          return ShapeSpec(channels=o)
      else:
          return ShapeSpec(channels=o[0], height=o[1], width=o[2])
  
file_path: detectron2/detectron2/modeling/roi_heads/box_head.py
imports: import numpy
  from typing import List
  import fvcore.nn.weight_init
  import torch
  from torch import nn
  from detectron2.config import configurable
  from detectron2.layers import Conv2d, ShapeSpec, get_norm
  from detectron2.utils.registry import Registry
label_desc: Use image node_id 4 for calling the FastRCNNConvFCHead obj's underlying member method code with example usage: obj.output_shape() and returns: ShapeSpec, ShapeSpec