class_docstr: A simple, general purpose, fully connected network
class_docstr_tok: ["A","simple",",","general","purpose",",","fully","connected","network"]
code: def __init__(self, d_inputs, d_outputs, lagged_reg_layers=[]):
      # Perform initialization of the pytorch superclass
      super(DeepNet, self).__init__()
      self.layers = nn.ModuleList()
      for d_hidden_i in lagged_reg_layers:
          self.layers.append(nn.Linear(d_inputs, d_hidden_i, bias=True))
          d_inputs = d_hidden_i
      self.layers.append(nn.Linear(d_inputs, d_outputs, bias=True))
      for lay in self.layers:
          nn.init.kaiming_normal_(lay.weight, mode="fan_in")
  
imports: import logging
  import math
  from collections import OrderedDict
  from functools import reduce
  from typing import Dict, List, Optional, Union
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from neuralprophet import configure, np_types
  from neuralprophet.components.router import get_future_regressors, get_seasonality, get_trend
  from neuralprophet.utils import check_for_regularization, config_events_to_model_dims, reg_func_events, reg_func_regressors, reg_func_season, reg_func_trend
  from neuralprophet.utils_torch import init_parameter, interprete_model
label_desc: Use image node_id 1 to create a new DeepNet object from inherited base classes: nn with example: obj = DeepNet(d_inputs, d_outputs, lagged_reg_layers)