code: def __init__(
      self, model: VAE, sampler_config: TwoStageVAESamplerConfig
  ):
      BaseDecoder.__init__(self)
  
      self.gamma_z = nn.Parameter(torch.ones(1, 1), requires_grad=True)
  
      layers = []
  
      layers.append(
          nn.Sequential(
              nn.Linear(
                  model.latent_dim, sampler_config.second_layers_dim
              ),
              nn.ReLU(),
          )
      )
  
      for i in range(sampler_config.second_stage_depth - 1):
          layers.append(
              nn.Sequential(
                  nn.Linear(
                      sampler_config.second_layers_dim,
                      sampler_config.second_layers_dim,
                  ),
                  nn.ReLU(),
              )
          )
  
      self.layers = nn.Sequential(*layers)
      self.reconstruction = nn.Linear(
          sampler_config.second_layers_dim, model.latent_dim
      )
  
imports: import os
  import shutil
  from typing import Union
  import numpy
  import torch
  import torch.nn
  from torch.utils.data import DataLoader, Dataset
  from ...data.datasets import collate_dataset_output
  from ...data.preprocessors import DataProcessor
  from ...models import VAE, VAEConfig
  from ...models.base.base_utils import ModelOutput
  from ...models.nn import BaseDecoder, BaseEncoder
  from ...trainers import BaseTrainer, BaseTrainerConfig
  from ..base import BaseSampler
  from .two_stage_sampler_config import TwoStageVAESamplerConfig
label_desc: Use image node_id 1 to create a new SecondDecoder object from inherited base classes: BaseDecoder with example: obj = SecondDecoder(model, sampler_config)