class_docstr: Hybrid Attention layer: combine time domain self-attention layer and frequency domain attention layer.
  
  Args:
      input_tensor (torch.Tensor): the input of the multi-head Hybrid Attention layer
      attention_mask (torch.Tensor): the attention mask for input tensor
  
  Returns:
      hidden_states (torch.Tensor): the output of the multi-head Hybrid Attention layer
class_docstr_tok: ["Hybrid","Attention","layer",":","combine","time","domain","self-attention","layer","and","frequency","domain","attention","layer",".","Args",":","input_tensor","(","torch.Tensor",")",":","the","input","of","the","multi-head","Hybrid","Attention","layer","attention_mask","(","torch.Tensor",")",":","the","attention","mask","for","input","tensor","Returns",":","hidden_states","(","torch.Tensor",")",":","the","output","of","the","multi-head","Hybrid","Attention","layer"]
code: def transpose_for_scores(self, x):
      new_x_shape = x.size()[:-1] + (
          self.num_attention_heads,
          self.attention_head_size,
      )
      x = x.view(*new_x_shape)
      # return x.permute(0, 2, 1, 3)
      return x
  
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 HybridAttention obj's underlying member method code with example usage: obj.transpose_for_scores(x) and returns: x