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 forward(
      self,
      x: Tensor,
      index: Optional[Tensor] = None,
      ptr: Optional[Tensor] = None,
      dim_size: Optional[int] = None,
      dim: int = -2,
  ) -> Tensor:
      x, _ = self.to_dense_batch(
          x,
          index,
          ptr,
          dim_size,
          dim,
          max_num_elements=self.max_num_elements,
      )
  
      return self.mlp(
          x.view(-1, x.size(1) * x.size(2)), index, dim_size
      )
  
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 3 for calling the MLPAggregation obj's underlying member method code with example usage: obj.forward(x, index, ptr, dim_size, dim) and returns: self