class_docstr: ## FFN module with [FTA](index.html) activation
class_docstr_tok: ["#","#","FFN","module","with","[","FTA","]","(","index.html",")","activation"]
code: def forward(self, x: torch.Tensor):
      # $f(x W_1 + b_1)$
      x = self.activation(self.layer1(x))
      # Apply dropout
      x = self.dropout(x)
      #
      return self.layer2(x)
  
imports: import copy
  import torch
  import torch.nn
  from labml import experiment
  from labml.configs import option
  from labml_helpers.module import Module
  from labml_nn.activations.fta import FTA
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.transformers import MultiHeadAttention, TransformerLayer
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 2 for calling the FeedForwardFTA obj's underlying member method code with example usage: obj.forward(x) and returns: self