code: def forward(self, x, output_layer_levels: List[int] = None):
      output = ModelOutput()
  
      out = torch.tanh(self.fc1(x.reshape(x.shape[0], -1)))
      out = torch.tanh(self.fc2(out))
      output["embedding"] = self.embedding(out)
      output["log_covariance"] = self.log_var(out)
  
      return output
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from torch.utils.data import Dataset
  from pythae.data.datasets import DatasetOutput
  from pythae.models import CIWAE, AutoModel, CIWAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj.forward(x, output_layer_levels) and returns: output