class_docstr: Feature extractor with list return
  See docstring for FeatureDictNet above, this class exists only to appease Torchscript typing constraints.
  In eager Python we could have returned List[Tensor] vs Dict[id, Tensor] based on a member bool.
class_docstr_tok: ["Feature","extractor","with","list","return","See","docstring","for","FeatureDictNet","above",",","this","class","exists","only","to","appease","Torchscript","typing","constraints",".","In","eager","Python","we","could","have","returned","List","[","Tensor","]","vs","Dict","[","id",",","Tensor","]","based","on","a","member","bool","."]
code: def __init__(
      self,
      model,
      out_indices=(0, 1, 2, 3, 4),
      out_map=None,
      feature_concat=False,
      flatten_sequential=False,
  ):
      super(FeatureListNet, self).__init__(
          model,
          out_indices=out_indices,
          out_map=out_map,
          feature_concat=feature_concat,
          flatten_sequential=flatten_sequential,
      )
  
imports: from collections import OrderedDict, defaultdict
  from copy import deepcopy
  from functools import partial
  from typing import Dict, List, Tuple
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new FeatureListNet object from inherited base classes: FeatureDictNet with example: obj = FeatureListNet(model, out_indices, out_map, feature_concat, flatten_sequential)