class_docstr: One transformer layer consists of a multi-head self-attention layer and a point-wise feed-forward layer.
  
  Args:
      hidden_states (torch.Tensor): the input of the multi-head self-attention sublayer
      attention_mask (torch.Tensor): the attention mask for the multi-head self-attention sublayer
  
  Returns:
      feedforward_output (torch.Tensor): The output of the point-wise feed-forward sublayer,
                                         is the output of the transformer layer.
class_docstr_tok: ["One","transformer","layer","consists","of","a","multi-head","self-attention","layer","and","a","point-wise","feed-forward","layer",".","Args",":","hidden_states","(","torch.Tensor",")",":","the","input","of","the","multi-head","self-attention","sublayer","attention_mask","(","torch.Tensor",")",":","the","attention","mask","for","the","multi-head","self-attention","sublayer","Returns",":","feedforward_output","(","torch.Tensor",")",":","The","output","of","the","point-wise","feed-forward","sublayer",",","is","the","output","of","the","transformer","layer","."]
code: def forward(self, hidden_states, attention_mask):
      attention_output = self.hybrid_attention(
          hidden_states, attention_mask
      )
      feedforward_output = self.feed_forward(attention_output)
  
      return feedforward_output
  
imports: import random
  import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  import math
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.data.interaction import Interaction
label_desc: Use image node_id 2 for calling the FEABlock obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask) and returns: feedforward_output