class_docstr: Class that combines a list of DeepConvexNet and ActNorm
  layers and provides energy score computation
  
  This class is based on SequentialFlow of the CP-Flow repo
  (https://github.com/CW-Huang/CP-Flow)
  
  Parameters
  ----------
  networks
      list of DeepConvexNet and/or ActNorm instances
class_docstr_tok: ["Class","that","combines","a","list","of","DeepConvexNet","and","ActNorm","layers","and","provides","energy","score","computation","This","class","is","based","on","SequentialFlow","of","the","CP-Flow","repo","(","https",":","\/\/github.com\/CW-Huang\/CP-Flow",")","Parameters","--","--","--","--","--","networks","list","of","DeepConvexNet","and\/or","ActNorm","instances"]
code: def __init__(self, networks: List[torch.nn.Module]) -> None:
      super().__init__(networks)
      self.networks = self.flows
  
file_path: gluonts/src/gluonts/torch/model/mqf2/icnn_utils.py
imports: from typing import Optional, Tuple, List, Union
  from cpflows.flows import SequentialFlow, DeepConvexFlow
  import torch
  import torch.nn.functional
  from torch.distributions.normal import Normal
label_desc: Use image node_id 1 to create a new SequentialNet object from inherited base classes: SequentialFlow with example: obj = SequentialNet(networks)