class_docstr: RegNet model.
  
  Paper: https://arxiv.org/abs/2003.13678
  Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
  and refer to: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/regnet.py
class_docstr_tok: ["RegNet","model",".","Paper",":","https",":","\/\/arxiv.org\/abs\/2003.13678","Original","Impl",":","https",":","\/\/github.com\/facebookresearch\/pycls\/blob\/master\/pycls\/models\/regnet.py","and","refer","to",":","https",":","\/\/github.com\/rwightman\/pytorch-image-models\/blob\/master\/timm\/models\/regnet.py"]
code: def _build_stage_info(cfg: dict):
      wa, w0, wm, d = cfg["wa"], cfg["w0"], cfg["wm"], cfg["depth"]
      widths, num_stages = generate_width_depth(wa, w0, wm, d)
  
      stage_widths, stage_depths = np.unique(widths, return_counts=True)
      stage_groups = [cfg["group_w"] for _ in range(num_stages)]
      stage_widths, stage_groups = adjust_width_groups_comp(
          stage_widths, stage_groups
      )
  
      info = []
      for i in range(num_stages):
          info.append(
              dict(
                  out_c=stage_widths[i],
                  depth=stage_depths[i],
                  group_width=stage_groups[i],
                  se_ratio=cfg["se_ratio"],
              )
          )
  
      return info
  
imports: from typing import Optional
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
label_desc: Use image node_id 3 for calling the RegNet obj's underlying member method code with example usage: obj._build_stage_info(cfg) and returns: info