class_docstr: AutoInt is a novel CTR prediction model based on self-attention mechanism,
  which can automatically learn high-order feature interactions in an explicit fashion.
class_docstr_tok: ["AutoInt","is","a","novel","CTR","prediction","model","based","on","self-attention","mechanism",",","which","can","automatically","learn","high-order","feature","interactions","in","an","explicit","fashion","."]
docstr: Get the attention-based feature interaction score
  
  Args:
      infeature (torch.FloatTensor): input feature embedding tensor. shape of[batch_size,field_size,embed_dim].
  
  Returns:
      torch.FloatTensor: Result of score. shape of [batch_size,1] .
docstr_tok: ["Get","the","attention-based","feature","interaction","score","Args",":","infeature","(","torch.FloatTensor",")",":","input","feature","embedding","tensor",".","shape","of","[","batch_size",",","field_size",",","embed_dim","]",".","Returns",":","torch.FloatTensor",":","Result","of","score",".","shape","of","[","batch_size,1","]","."]
code: def autoint_layer(self, infeature):
      """Get the attention-based feature interaction score
  
      Args:
          infeature (torch.FloatTensor): input feature embedding tensor. shape of[batch_size,field_size,embed_dim].
  
      Returns:
          torch.FloatTensor: Result of score. shape of [batch_size,1] .
      """
  
      att_infeature = self.att_embedding(infeature)
      cross_term = att_infeature.transpose(0, 1)
      for self_attn in self.self_attns:
          cross_term, _ = self_attn(cross_term, cross_term, cross_term)
      cross_term = cross_term.transpose(0, 1)
      # Residual connection
      if self.has_residual:
          v_res = self.v_res_embedding(infeature)
          cross_term += v_res
      # Interacting layer
      cross_term = (
          F.relu(cross_term)
          .contiguous()
          .view(-1, self.atten_output_dim)
      )
      batch_size = infeature.shape[0]
      att_output = self.attn_fc(cross_term) + self.deep_predict_layer(
          self.mlp_layers(infeature.view(batch_size, -1))
      )
      return att_output
  
file_path: RecBole/recbole/model/context_aware_recommender/autoint.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.layers import MLPLayers
label_desc: Use image node_id 3 for calling the AutoInt obj's underlying member method code with example usage: obj.autoint_layer(infeature) and returns: att_output