code: def forward(self, x):
      cur_output = x
      for i, now_layer in enumerate(self.dnn_layers):
          cur_output = now_layer(cur_output)
      return cur_output
  
imports: from __future__ import division
  from __future__ import print_function
  from collections import defaultdict
  import os
  import gc
  import numpy
  import pandas
  from typing import Callable, Optional, Text, Union
  from sklearn.metrics import roc_auc_score, mean_squared_error
  import torch
  import torch.nn
  import torch.optim
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...data.dataset.weight import Reweighter
  from ...utils import auto_filter_kwargs, init_instance_by_config, unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path
  from ...log import get_module_logger
  from ...workflow import R
  from qlib.contrib.meta.data_selection.utils import ICLoss
  from torch.nn import DataParallel
label_desc: Use image node_id 3 for calling the Net obj's underlying member method code with example usage: obj.forward(x) and returns: cur_output