code: def __init__(
      self,
      input_size,
      hidden_size=None,
      output_size=None,
      num_layers=2,
      dropout=0.0,
  ):
      super().__init__()
      self.input_size = input_size
      self.output_size = (
          self.input_size if output_size is None else output_size
      )
      self.hidden_size = (
          self.output_size if hidden_size is None else hidden_size
      )
      self.num_layers = num_layers
      self.activation_type = "relu"
      self.dropout = dropout
      self.layers = nn.ModuleList()
      if num_layers == 1:
          self.layers.append(Linear(self.input_size, self.output_size))
      else:
          self.layers.append(Linear(self.input_size, self.hidden_size))
          for _ in range(1, num_layers - 1):
              self.layers.append(
                  Linear(self.hidden_size, self.hidden_size)
              )
          self.layers.append(Linear(self.hidden_size, self.output_size))
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.models import FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import AdaptiveSoftmax, LearnedPositionalEmbedding, MultiheadAttention, SinusoidalPositionalEmbedding
  from pytorch_translate import transformer, vocab_reduction
  from pytorch_translate.average_attention import AverageAttention, AverageWindowAttention
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 1 to create a new FeedForwardNetwork object from inherited base classes: nn with example: obj = FeedForwardNetwork(input_size, hidden_size, output_size, num_layers, dropout)