class_docstr: The graph neural network operator from the `"Weisfeiler and Leman Go
  Neural: Higher-order Graph Neural Networks"
  <https://arxiv.org/abs/1810.02244>`_ paper.
  
  .. math::
      \mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2
      \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot \mathbf{x}_j
  
  where :math:`e_{j,i}` denotes the edge weight from source node :obj:`j` to
  target node :obj:`i` (default: :obj:`1`)
  
  Args:
      in_channels (int or tuple): Size of each input sample, or :obj:`-1` to
          derive the size from the first input(s) to the forward method.
          A tuple corresponds to the sizes of source and target
          dimensionalities.
      out_channels (int): Size of each output sample.
      aggr (str, optional): The aggregation scheme to use
          (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`).
          (default: :obj:`"add"`)
      bias (bool, optional): If set to :obj:`False`, the layer will not learn
          an additive bias. (default: :obj:`True`)
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.conv.MessagePassing`.
  
  Shapes:
      - **input:**
        node features :math:`(|\mathcal{V}|, F_{in})` or
        :math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))`
        if bipartite,
        edge indices :math:`(2, |\mathcal{E}|)`,
        edge weights :math:`(|\mathcal{E}|)` *(optional)*
      - **output:** node features :math:`(|\mathcal{V}|, F_{out})` or
        :math:`(|\mathcal{V}_t|, F_{out})` if bipartite
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code: def message_and_aggregate(
      self, adj_t: Adj, x: OptPairTensor
  ) -> Tensor:
      return spmm(adj_t, x[0], reduce=self.aggr)
  
file_path: pytorch_geometric/torch_geometric/nn/conv/graph_conv.py
imports: from typing import Tuple, Union
  from torch import Tensor
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.dense.linear import Linear
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size
  from torch_geometric.utils import spmm
label_desc: Use image node_id 5 for calling the GraphConv obj's underlying member method code with example usage: obj.message_and_aggregate(adj_t, x) and returns: spmm