class_docstr: This is a abstract sequential recommender. All the sequential model should implement This class.
class_docstr_tok: ["This","is","a","abstract","sequential","recommender",".","All","the","sequential","model","should","implement","This","class","."]
docstr: Gathers the vectors at the specific positions over a minibatch
docstr_tok: ["Gathers","the","vectors","at","the","specific","positions","over","a","minibatch"]
code: def gather_indexes(self, output, gather_index):
      """Gathers the vectors at the specific positions over a minibatch"""
      gather_index = gather_index.view(-1, 1, 1).expand(
          -1, -1, output.shape[-1]
      )
      output_tensor = output.gather(dim=1, index=gather_index)
      return output_tensor.squeeze(1)
  
imports: from logging import getLogger
  import numpy
  import torch
  import torch.nn
  from recbole.model.layers import FMEmbedding, FMFirstOrderLinear, FLEmbedding
  from recbole.utils import ModelType, InputType, FeatureSource, FeatureType, set_color
label_desc: Use image node_id 2 for calling the SequentialRecommender obj's underlying member method code with example usage: obj.gather_indexes(output, gather_index) and returns: output_tensor