class_docstr: HighResolutionNet feature extraction
  
  The design of HRNet makes it easy to grab feature maps, this class provides a simple wrapper to do so.
  It would be more complicated to use the FeatureNet helpers.
  
  The `feature_location=incre` allows grabbing increased channel count features using part of the
  classification head. If `feature_location=''` the default HRNet features are returned. First stem
  conv is used for stride 2 features.
class_docstr_tok: ["HighResolutionNet","feature","extraction","The","design","of","HRNet","makes","it","easy","to","grab","feature","maps",",","this","class","provides","a","simple","wrapper","to","do","so",".","It","would","be","more","complicated","to","use","the","FeatureNet","helpers",".","The","`","feature_location=incre","`","allows","grabbing","increased","channel","count","features","using","part","of","the","classification","head",".","If","`","feature_location=","''","`","the","default","HRNet","features","are","returned",".","First","stem","conv","is","used","for","stride","2","features","."]
code: def forward_features(self, x):
      assert False, "Not supported"
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._features import FeatureInfo
  from ._registry import register_model, generate_default_cfgs
  from .resnet import BasicBlock, Bottleneck
label_desc: Use image node_id 2 for calling the HighResolutionNetFeatures obj's underlying member method code with example usage: obj.forward_features(x) without return types