class_docstr: Performs MLP aggregation in which the elements to aggregate are
  flattened into a single vectorial representation, and are then processed by
  a Multi-Layer Perceptron (MLP), as described in the `"Graph Neural Networks
  with Adaptive Readouts" <https://arxiv.org/abs/2211.04952>`_ paper.
  
  .. note::
  
      :class:`GRUAggregation` requires sorted indices :obj:`index` as input.
      Specifically, if you use this aggregation as part of
      :class:`~torch_geometric.nn.conv.MessagePassing`, ensure that
      :obj:`edge_index` is sorted by destination nodes, either by manually
      sorting edge indices via :meth:`~torch_geometric.utils.sort_edge_index`
      or by calling :meth:`torch_geometric.data.Data.sort`.
  
  .. warning::
  
      :class:`MLPAggregation` is not a permutation-invariant operator.
  
  Args:
      in_channels (int): Size of each input sample.
      out_channels (int): Size of each output sample.
      max_num_elements (int): The maximum number of elements to aggregate per
          group.
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.models.MLP`.
code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}({self.in_channels}, "
          f"{self.out_channels}, "
          f"max_num_elements={self.max_num_elements})"
      )
  
file_path: pytorch_geometric/torch_geometric/nn/aggr/mlp.py
imports: from typing import Optional
  from torch import Tensor
  from torch_geometric.nn.aggr import Aggregation
label_desc: Use image node_id 4 for calling the MLPAggregation obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str, self, str