code: def forward(
      self,
      data_feature,
      real_attribute_input_noise,
      addi_attribute_input_noise,
      feature_input_noise,
      data_attribute,
  ):
      # since we still not support self.num_packing
      self.data_feature = data_feature
      self.data_attribute = data_attribute
  
      if self.data_feature[0].shape[1] % self.sample_len != 0:
          from bigdl.nano.utils.common import invalidInputError
  
          invalidInputError(
              False, "length must be a multiple of sample_len"
          )
      self.sample_time = int(
          self.data_feature[0].shape[1] / self.sample_len
      )
      self.sample_feature_dim = self.data_feature[0].shape[2]
      self.sample_attribute_dim = self.data_attribute[0].shape[1]
  
      self.batch_size = self.data_feature[0].shape[0]
  
      # generate training route (fake)
      self.g_output_feature_train_tf_l = []
      self.g_output_attribute_train_tf_l = []
  
      for i in range(self.num_packing):
          (
              g_output_feature_train_tf,
              g_output_attribute_train_tf,
              _,
              _,
              _,
          ) = self.generator(
              real_attribute_input_noise[i],
              addi_attribute_input_noise[i],
              feature_input_noise[i],
              data_feature[i],
          )
          self.g_output_feature_train_tf_l.append(
              g_output_feature_train_tf
          )
          self.g_output_attribute_train_tf_l.append(
              g_output_attribute_train_tf
          )
  
      self.g_feature_train = torch.cat(
          self.g_output_feature_train_tf_l, dim=1
      )
      self.g_attribute_train = torch.cat(
          self.g_output_attribute_train_tf_l, dim=1
      )
  
      self.d_fake_train_tf = self.discriminator(
          self.g_feature_train, self.g_attribute_train
      )
      self.attr_d_fake_train_tf = self.attr_discriminator(
          self.g_attribute_train
      )
  
      # generate training route (real)
      self.real_feature_pl = torch.cat(self.data_feature, dim=1)
      self.real_attribute_pl = torch.cat(self.data_attribute, dim=1)
      self.d_real_train_tf = self.discriminator(
          self.real_feature_pl, self.real_attribute_pl
      )
      self.attr_d_real_train_tf = self.attr_discriminator(
          self.real_attribute_pl
      )
  
      return (
          self.d_fake_train_tf,
          self.attr_d_fake_train_tf,
          self.d_real_train_tf,
          self.attr_d_real_train_tf,
      )
  
file_path: BigDL/python/chronos/src/bigdl/chronos/simulator/doppelganger/doppelganger.py
imports: import numpy
  from tqdm import tqdm
  import datetime
  import os
  import math
  import sys
  import torch
  import torch.nn
  from .network import Discriminator, AttrDiscriminator, DoppelGANgerGenerator, RNNInitialStateType
label_desc: Use image node_id 2 for calling the DoppelGANger obj's underlying member method code with example usage: obj.forward(data_feature, real_attribute_input_noise, addi_attribute_input_noise, feature_input_noise, data_attribute) and returns: self,  self,  self,  self