code: def radial_flow(self, model_configs, demo_data):
      model_configs.input_dim = tuple(demo_data["data"][0].shape)
      return RadialFlow(model_configs)
  
imports: import os
  import shutil
  from copy import deepcopy
  import numpy
  import pytest
  import torch
  from torch.optim import Adam
  from pythae.data.datasets import BaseDataset
  from pythae.models import AutoModel
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.normalizing_flows import NFModel, RadialFlow, RadialFlowConfig
  from pythae.pipelines import TrainingPipeline
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 2 for calling the Test_Model_forward obj's underlying member method code with example usage: obj.radial_flow(model_configs, demo_data) and returns: RadialFlow