class_docstr: The Set2Set aggregation operator based on iterative content-based
  attention, as described in the `"Order Matters: Sequence to sequence for
  Sets" <https://arxiv.org/abs/1511.06391>`_ paper.
  
  .. math::
      \mathbf{q}_t &= \mathrm{LSTM}(\mathbf{q}^{*}_{t-1})
  
      \alpha_{i,t} &= \mathrm{softmax}(\mathbf{x}_i \cdot \mathbf{q}_t)
  
      \mathbf{r}_t &= \sum_{i=1}^N \alpha_{i,t} \mathbf{x}_i
  
      \mathbf{q}^{*}_t &= \mathbf{q}_t \, \Vert \, \mathbf{r}_t,
  
  where :math:`\mathbf{q}^{*}_T` defines the output of the layer with twice
  the dimensionality as the input.
  
  Args:
      in_channels (int): Size of each input sample.
      processing_steps (int): Number of iterations :math:`T`.
      **kwargs (optional): Additional arguments of :class:`torch.nn.LSTM`.
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code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}({self.in_channels}, "
          f"{self.out_channels})"
      )
  
file_path: pytorch_geometric/torch_geometric/nn/aggr/set2set.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch_geometric.nn.aggr import Aggregation
  from torch_geometric.utils import softmax
label_desc: Use image node_id 4 for calling the Set2Set obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str