code: def __init__(
      self, input_dim, output_dim=1, layers=(256,), act="LeakyReLU"
  ):
      super(Net, self).__init__()
  
      layers = [input_dim] + list(layers)
      dnn_layers = []
      drop_input = nn.Dropout(0.05)
      dnn_layers.append(drop_input)
      hidden_units = input_dim
      for i, (_input_dim, hidden_units) in enumerate(
          zip(layers[:-1], layers[1:])
      ):
          fc = nn.Linear(_input_dim, hidden_units)
          if act == "LeakyReLU":
              activation = nn.LeakyReLU(
                  negative_slope=0.1, inplace=False
              )
          elif act == "SiLU":
              activation = nn.SiLU()
          else:
              raise NotImplementedError(
                  f"This type of input is not supported"
              )
          bn = nn.BatchNorm1d(hidden_units)
          seq = nn.Sequential(fc, bn, activation)
          dnn_layers.append(seq)
      drop_input = nn.Dropout(0.05)
      dnn_layers.append(drop_input)
      fc = nn.Linear(hidden_units, output_dim)
      dnn_layers.append(fc)
      # optimizer  # pylint: disable=W0631
      self.dnn_layers = nn.ModuleList(dnn_layers)
      self._weight_init()
  
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 1 to create a new Net object from inherited base classes: nn with example: obj = Net(input_dim, output_dim, layers, act)