class_docstr: Interface for computing summary representations from multiple variable-sized sets of representations.
docstr: Returns: float tensor of shape [num_samples, D']
docstr_tok: ["Returns",":","float","tensor","of","shape","[","num_samples",",","D","'","]"]
code: def forward(
      self, inputs: ElementsToSummaryRepresentationInput
  ) -> torch.Tensor:
      """Returns: float tensor of shape [num_samples, D']"""
  
imports: from typing_extensions import Literal
  import torch
  from abc import abstractmethod
  from math import sqrt
  from torch import nn
  from torch_scatter import scatter, scatter_log_softmax, scatter_sum
  from typing import NamedTuple, Union
label_desc: Use image node_id 1 for calling the AbstractVarSizedElementReduce obj's underlying member method code with example usage: obj.forward(inputs) without return types