code: def forward(self, z: torch.Tensor):
      out = self.layers(z)
  
      output = ModelOutput(
          embedding=self.mu(out), log_covariance=self.std(out)
      )
  
      return output
  
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 2 for calling the SecondEncoder obj's underlying member method code with example usage: obj.forward(z) and returns: output