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 forward(
      self, x: torch.Tensor, context: Optional[torch.Tensor] = None
  ) -> torch.Tensor:
      for network in self.networks:
          if isinstance(network, DeepConvexNet):
              x = network.forward(x, context=context)
          else:
              x = network.forward(x)
      return x
  
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 2 for calling the SequentialNet obj's underlying member method code with example usage: obj.forward(x, context) and returns: x