class_docstr: Tests based on a more non-standard model
  
  Specifically, add modules with non-standard names and non-standard outputs
  like tuples or dicts.
  
  This test class does not re-iterate all the tests performed on the standard
  model but focuses on the parts that change, e.g. how the outputs are
  recorded.
class_docstr_tok: ["Tests","based","on","a","more","non-standard","model","Specifically",",","add","modules","with","non-standard","names","and","non-standard","outputs","like","tuples","or","dicts",".","This","test","class","does","not","re-iterate","all","the","tests","performed","on","the","standard","model","but","focuses","on","the","parts","that","change",",","e.g",".","how","the","outputs","are","recorded","."]
code: def doctor(
      self, module0_cls, criterion_cls, net_cls, doctor_cls, data
  ):
      # the passed module doesn't matter as they are hard-coded
      torch.manual_seed(0)
      net = net_cls(
          module0_cls,
          criterion=criterion_cls,
          max_epochs=3,
          batch_size=32,
      )
      doctor = doctor_cls(net)
      doctor.fit(*data)
      return doctor
  
imports: import itertools
  from unittest import mock
  import numpy
  import pytest
  import torch
  from torch import nn
label_desc: Use image node_id 8 for calling the TestSkorchDoctorComplexArchitecture obj's underlying member method code with example usage: obj.doctor(module0_cls, criterion_cls, net_cls, doctor_cls, data) and returns: doctor