class_docstr: A simple, general purpose, fully connected network
class_docstr_tok: ["A","simple",",","general","purpose",",","fully","connected","network"]
docstr: This method defines the network layering and activation functions
docstr_tok: ["This","method","defines","the","network","layering","and","activation","functions"]
code: def forward(self, x):
      """
      This method defines the network layering and activation functions
      """
      activation = nn.functional.relu
      for i in range(len(self.layers)):
          if i > 0:
              x = activation(x)
          x = self.layers[i](x)
      return x
  
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 2 for calling the DeepNet obj's underlying member method code with example usage: obj.forward(x) and returns: x