class_docstr: Create message passing layers for the MOGONET method. Each layer is defined as:
  
  .. math::
      H^{(l+1)}=f(H^{(l)}, A) = \sigma(AH^{(l)}W^{(l)})
  
  where :math:`\mathbf{H^{(l)}}` is the input of the :math:`l`-th layer and :math:`\mathbf{W^{(l)}}` is the weight
  matrix of the :math:`l`-th layer. :math:`\sigma(.)` denotes a non-linear activation function.
  
  For more information please refer to the MOGONET paper.
  
  Args:
      in_channels (int): Size of each input sample.
      out_channels (int): Size of each output sample.
      bias (bool, optional): If set to ``False``, the layer will not learn an additive bias. (default: ``True``)
      aggr (string or list or Aggregation, optional): The aggregation scheme to use,
          *e.g.*, ``"add"``, ``"sum"``, ``"mean"``, ``"min"``, ``"max"`` or ``"mul"``.
      **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`.
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      bias: bool = True,
      aggr: Optional[Union[str, List[str], Aggregation]] = "add",
      **kwargs,
  ) -> None:
      super().__init__(aggr=aggr, **kwargs)
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.weight = Parameter(
          torch.Tensor(self.in_channels, self.out_channels)
      )
  
      if bias:
          self.bias = Parameter(torch.Tensor(self.out_channels))
      else:
          self.register_parameter("bias", None)
  
      self.reset_parameters()
  
imports: from typing import List, Optional, Union
  import torch
  import torch.nn.functional
  import torch_sparse
  from torch import Tensor
  from torch.nn import Module, Parameter
  from torch.nn.init import xavier_normal_
  from torch_geometric.nn.aggr import Aggregation
  from torch_geometric.nn.conv import MessagePassing
  from torch_sparse import SparseTensor
label_desc: Use image node_id 1 to create a new MogonetGCNConv object from inherited base classes: MessagePassing with example: obj = MogonetGCNConv(in_channels, out_channels, bias, aggr)