code: def test_return_sampling_without_eval(
      self,
      model,
      dummy_data,
      training_config,
      sampler,
      num_sample_and_batch_size,
  ):
      num_samples, batch_size = (
          num_sample_and_batch_size[0],
          num_sample_and_batch_size[1],
      )
  
      start_gamma = deepcopy(sampler.second_vae.decoder.gamma_z)
  
      sampler.fit(
          train_data=dummy_data,
          eval_data=None,
          training_config=training_config,
      )
  
      final_gamma = deepcopy(sampler.second_vae.decoder.gamma_z)
  
      gen_samples = sampler.sample(
          num_samples=num_samples,
          batch_size=batch_size,
          return_gen=True,
      )
  
      assert gen_samples.shape[0] == num_samples
      assert start_gamma != final_gamma
  
imports: import os
  from copy import deepcopy
  import numpy
  import pytest
  import torch
  from pythae.models import AE, VAE, VAMP, AEConfig, VAEConfig, VAMPConfig
  from pythae.pipelines import GenerationPipeline
  from pythae.samplers import NormalSampler, NormalSamplerConfig, TwoStageVAESampler, TwoStageVAESamplerConfig
  from pythae.trainers import BaseTrainerConfig
  from pythae.data.preprocessors import DataProcessor
label_desc: Use image node_id 3 for calling the Test_TwoStageVAESampler_Sampling obj's underlying member method code with example usage: obj.test_return_sampling_without_eval(model, dummy_data, training_config, sampler, num_sample_and_batch_size) without return types