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
code: def __init__(self, networks: List[torch.nn.Module]) -> None:
      super().__init__(networks)
      self.networks = self.flows
  
imports: from typing import List, Optional, Tuple
  from cpflows.flows import DeepConvexFlow, SequentialFlow
  import torch
  from torch.distributions import AffineTransform, Distribution, Normal, TransformedDistribution
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SequentialNet object from inherited base classes: SequentialFlow with example: obj = SequentialNet(networks)