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"]
docstr: SpeedUp version of Autocorrelation (a batch-normalization style design)
  This is for the inference phase.
docstr_tok: ["SpeedUp","version","of","Autocorrelation","(","a","batch-normalization","style","design",")","This","is","for","the","inference","phase","."]
code: def time_delay_agg_inference(self, values, corr):
      """
      SpeedUp version of Autocorrelation (a batch-normalization style design)
      This is for the inference phase.
      """
      batch = values.shape[0]
      head = values.shape[1]
      channel = values.shape[2]
      length = values.shape[3]
      # index init
      init_index = (
          torch.arange(length)
          .unsqueeze(0)
          .unsqueeze(0)
          .unsqueeze(0)
          .repeat(batch, head, channel, 1)
          .to(values.device)
      )
      # find top k
      top_k = int(self.factor * math.log(length))
      mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)
      weights, delay = torch.topk(mean_value, top_k, dim=-1)
      # update corr
      tmp_corr = torch.softmax(weights, dim=-1)
      # aggregation
      tmp_values = values.repeat(1, 1, 1, 2)
      delays_agg = torch.zeros_like(values).float()
      for i in range(top_k):
          tmp_delay = init_index + delay[:, i].unsqueeze(1).unsqueeze(
              1
          ).unsqueeze(1).repeat(1, head, channel, length)
          pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)
          delays_agg = delays_agg + pattern * (
              tmp_corr[:, i]
              .unsqueeze(1)
              .unsqueeze(1)
              .unsqueeze(1)
              .repeat(1, head, channel, length)
          )
      return delays_agg
  
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 4 for calling the HybridAttention obj's underlying member method code with example usage: obj.time_delay_agg_inference(values, corr) and returns: delays_agg