code: def __init__(
      self,
      data_feature_outputs,
      data_attribute_outputs,
      L_max,
      num_real_attribute,
      sample_len=10,
      discriminator_num_layers=5,
      discriminator_num_units=200,
      attr_discriminator_num_layers=5,
      attr_discriminator_num_units=200,
      attribute_num_units=100,
      attribute_num_layers=3,
      feature_num_units=100,
      feature_num_layers=1,
      attribute_input_noise_dim=5,
      addi_attribute_input_noise_dim=5,
      d_gp_coe=10,
      attr_d_gp_coe=10,
      g_attr_d_coe=1,
      d_lr=0.001,
      attr_d_lr=0.001,
      g_lr=0.001,
      g_rounds=1,
      d_rounds=1,
      **kwargs,
  ):
      super().__init__()
      self.automatic_optimization = False
      self.save_hyperparameters(
          "discriminator_num_layers",
          "discriminator_num_units",
          "attr_discriminator_num_layers",
          "attr_discriminator_num_units",
          "attribute_num_units",
          "attribute_num_layers",
          "feature_num_units",
          "feature_num_layers",
          "attribute_input_noise_dim",
          "addi_attribute_input_noise_dim",
          "d_gp_coe",
          "attr_d_gp_coe",
          "g_attr_d_coe",
          "d_lr",
          "attr_d_lr",
          "g_lr",
          "g_rounds",
          "d_rounds",
          "L_max",
          "sample_len",
          "num_real_attribute",
      )
      self.g_rounds = g_rounds
      self.d_rounds = d_rounds
      self.sample_len = sample_len
      self.L_max = L_max
      self.data_feature_outputs = data_feature_outputs
      self.data_attribute_outputs = data_attribute_outputs
  
      self.length = self.L_max // self.sample_len
      self.real_attribute_mask = [True] * num_real_attribute + [
          False
      ] * (len(data_attribute_outputs) - num_real_attribute)
      self.gen_flag_dims = []
      dim = 0
      for output in self.data_feature_outputs:
          if output.is_gen_flag:
              if output.dim != 2:
                  raise Exception("gen flag output's dim should be 2")
              self.gen_flag_dims = [dim, dim + 1]
              break
          dim += output.dim
      if len(self.gen_flag_dims) == 0:
          raise Exception("gen flag not found")
  
      # model init
      self.model = DoppelGANger(
          data_feature_outputs=self.data_feature_outputs,
          data_attribute_outputs=self.data_attribute_outputs,
          real_attribute_mask=self.real_attribute_mask,
          sample_len=self.sample_len,
          L_max=self.L_max,
          num_packing=1,  # any num other than 1 will be supported later
          discriminator_num_layers=self.hparams.discriminator_num_layers,
          discriminator_num_units=self.hparams.discriminator_num_units,
          attr_discriminator_num_layers=self.hparams.attr_discriminator_num_layers,
          attr_discriminator_num_units=self.hparams.attr_discriminator_num_units,
          attribute_num_units=self.hparams.attribute_num_units,
          attribute_num_layers=self.hparams.attribute_num_layers,
          feature_num_units=self.hparams.feature_num_units,
          feature_num_layers=self.hparams.feature_num_layers,
          attribute_input_noise_dim=self.hparams.attribute_input_noise_dim,
          addi_attribute_input_noise_dim=self.hparams.addi_attribute_input_noise_dim,
          initial_state=RNNInitialStateType.RANDOM,
      )
  
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 1 to create a new DoppelGANger_pl object from inherited base classes: LightningModule with example: obj = DoppelGANger_pl(data_feature_outputs, data_attribute_outputs, L_max, num_real_attribute, sample_len, discriminator_num_layers, discriminator_num_units, attr_discriminator_num_layers, attr_discriminator_num_units, attribute_num_units, attribute_num_layers, feature_num_units, feature_num_layers, attribute_input_noise_dim, addi_attribute_input_noise_dim, d_gp_coe, attr_d_gp_coe, g_attr_d_coe, d_lr, attr_d_lr, g_lr, g_rounds, d_rounds)