code: def sample_from(
      self,
      real_attribute_input_noise,
      addi_attribute_input_noise,
      feature_input_noise,
      feature_input_data,
      batch_size=32,
  ):
      features, attributes, gen_flags, lengths = self.model.sample_from(
          real_attribute_input_noise,
          addi_attribute_input_noise,
          feature_input_noise,
          feature_input_data,
          self.gen_flag_dims,
          batch_size=batch_size,
      )
      return features, attributes, gen_flags, lengths
  
imports: import os
  import numpy
  import sys
  from collections import OrderedDict
  import math
  import torch
  import torch.nn.functional
  from torch import nn
  from pytorch_lightning import LightningModule
  from pytorch_lightning import seed_everything
  from .doppelganger import DoppelGANger
  from .network import RNNInitialStateType
  from .loss import doppelganger_loss
  from .util import gen_attribute_input_noise, gen_feature_input_noise, gen_feature_input_data_free, renormalize_per_sample
label_desc: Use image node_id 5 for calling the DoppelGANger_pl obj's underlying member method code with example usage: obj.sample_from(real_attribute_input_noise, addi_attribute_input_noise, feature_input_noise, feature_input_data, batch_size) and returns: features,  attributes,  gen_flags,  lengths