code: def __init__(
      self,
      hidden_ch,
      num_hidden_layers,
      in_features,
      out_features,
      outermost_linear=False,
  ):
      super().__init__()
  
      self.net = []
      self.net.append(
          FCLayer(in_features=in_features, out_features=hidden_ch)
      )
  
      for i in range(num_hidden_layers):
          self.net.append(
              FCLayer(in_features=hidden_ch, out_features=hidden_ch)
          )
  
      if outermost_linear:
          self.net.append(
              nn.Linear(
                  in_features=hidden_ch, out_features=out_features
              )
          )
      else:
          self.net.append(
              FCLayer(in_features=hidden_ch, out_features=out_features)
          )
  
      self.net = nn.Sequential(*self.net)
      self.net.apply(self.init_weights)
  
imports: import torch
  import torch.nn
  import torchvision.utils
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new FCBlock object from inherited base classes: nn with example: obj = FCBlock(hidden_ch, num_hidden_layers, in_features, out_features, outermost_linear)