code: def prior(self, model_configs, request):
      device = "cuda" if torch.cuda.is_available() else "cpu"
  
      return torch.distributions.MultivariateNormal(
          torch.zeros(np.prod(model_configs.input_dim)).to(device),
          torch.eye(np.prod(model_configs.input_dim)).to(device),
      )
  
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 5 for calling the Test_RadialFlow_Training obj's underlying member method code with example usage: obj.prior(model_configs, request) and returns: torch