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 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
  
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 2 for calling the SequentialNet obj's underlying member method code with example usage: obj.forward(x, context) and returns: x