class_docstr: Point-wise feed-forward layer is implemented by two dense layers.
  
  Args:
      input_tensor (torch.Tensor): the input of the point-wise feed-forward layer
  
  Returns:
      hidden_states (torch.Tensor): the output of the point-wise feed-forward layer
class_docstr_tok: ["Point-wise","feed-forward","layer","is","implemented","by","two","dense","layers",".","Args",":","input_tensor","(","torch.Tensor",")",":","the","input","of","the","point-wise","feed-forward","layer","Returns",":","hidden_states","(","torch.Tensor",")",":","the","output","of","the","point-wise","feed-forward","layer"]
code: def forward(self, input_tensor):
      hidden_states = self.dense_1(input_tensor)
      hidden_states = self.intermediate_act_fn(hidden_states)
  
      hidden_states = self.dense_2(hidden_states)
      hidden_states = self.dropout(hidden_states)
      hidden_states = self.LayerNorm(hidden_states + input_tensor)
  
      return hidden_states
  
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 5 for calling the FeedForward obj's underlying member method code with example usage: obj.forward(input_tensor) and returns: hidden_states