code: def forward(self, x):
      x = self.layers[0](x)
      for layer in self.layers[1:]:
          x = F.relu(x)
          x = F.dropout(x, p=self.dropout, training=self.training)
          x = layer(x)
      return x
  
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 2 for calling the FeedForwardNetwork obj's underlying member method code with example usage: obj.forward(x) and returns: x