code: def sample_from(
      self,
      real_attribute_input_noise,
      addi_attribute_input_noise,
      feature_input_noise,
      feature_input_data,
      gen_flag_dims,
      batch_size=32,
  ):
      features = []
      attributes = []
      gen_flags = []
      lengths = []
      round_ = int(
          math.ceil(float(feature_input_noise.shape[0]) / batch_size)
      )
      assert self.training is False, "please call .eval() on the model"
      self.generator.eval()
      for i in range(round_):
          (feature, attribute, gen_flag, length, _) = self.generator(
              real_attribute_input_noise[
                  i * batch_size : (i + 1) * batch_size
              ],
              addi_attribute_input_noise[
                  i * batch_size : (i + 1) * batch_size
              ],
              feature_input_noise[
                  i * batch_size : (i + 1) * batch_size
              ],
              feature_input_data[i * batch_size : (i + 1) * batch_size],
          )
          features.append(feature)
          attributes.append(attribute)
          gen_flags.append(gen_flag)
          lengths.append(length)
      features = torch.cat(features, dim=0)
      attributes = torch.cat(attributes, dim=0)
      gen_flags = torch.cat(gen_flags, dim=0)
      lengths = torch.cat(lengths, dim=0)
      gen_flags = gen_flags[:, :, 0]
  
      features = features.detach().numpy()
      attributes = attributes.detach().numpy()
      gen_flags = gen_flags.detach().numpy()
      lengths = lengths.detach().numpy()
  
      features = np.delete(features, gen_flag_dims, axis=2)
  
      return features, attributes, gen_flags, lengths
  
file_path: analytics-zoo/pyzoo/zoo/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 3 for calling the DoppelGANger 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, gen_flag_dims, batch_size) and returns: features,  attributes,  gen_flags,  lengths