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 __init__(
      self,
      n_heads,
      hidden_size,
      intermediate_size,
      hidden_dropout_prob,
      attn_dropout_prob,
      hidden_act,
      layer_norm_eps,
      n,
      config,
  ):
      super(FEABlock, self).__init__()
      self.hybrid_attention = HybridAttention(
          n_heads,
          hidden_size,
          hidden_dropout_prob,
          attn_dropout_prob,
          layer_norm_eps,
          n,
          config,
      )
      self.feed_forward = FeedForward(
          hidden_size,
          intermediate_size,
          hidden_dropout_prob,
          hidden_act,
          layer_norm_eps,
      )
  
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 1 to create a new FEABlock object from inherited base classes: nn with example: obj = FEABlock(n_heads, hidden_size, intermediate_size, hidden_dropout_prob, attn_dropout_prob, hidden_act, layer_norm_eps, n, config)