class_docstr: The modified :class:`GINConv` operator from the `"Strategies for
  Pre-training Graph Neural Networks" <https://arxiv.org/abs/1905.12265>`_
  paper
  
  .. math::
      \mathbf{x}^{\prime}_i = h_{\mathbf{\Theta}} \left( (1 + \epsilon) \cdot
      \mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathrm{ReLU}
      ( \mathbf{x}_j + \mathbf{e}_{j,i} ) \right)
  
  that is able to incorporate edge features :math:`\mathbf{e}_{j,i}` into
  the aggregation procedure.
  
  Args:
      nn (torch.nn.Module): A neural network :math:`h_{\mathbf{\Theta}}` that
          maps node features :obj:`x` of shape :obj:`[-1, in_channels]` to
          shape :obj:`[-1, out_channels]`, *e.g.*, defined by
          :class:`torch.nn.Sequential`.
      eps (float, optional): (Initial) :math:`\epsilon`-value.
          (default: :obj:`0.`)
      train_eps (bool, optional): If set to :obj:`True`, :math:`\epsilon`
          will be a trainable parameter. (default: :obj:`False`)
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.conv.MessagePassing`.
code: def forward(
      self,
      x: Union[Tensor, OptPairTensor],
      edge_index: Adj,
      edge_attr: OptTensor = None,
      size: Size = None,
  ) -> Tensor:
      """"""
      if isinstance(x, Tensor):
          x: OptPairTensor = (x, x)
  
      # Node and edge feature dimensionalites need to match.
      if isinstance(edge_index, Tensor):
          assert edge_attr is not None
          assert x[0].size(-1) == edge_attr.size(-1)
      elif isinstance(edge_index, SparseTensor):
          assert x[0].size(-1) == edge_index.size(-1)
  
      # propagate_type: (x: OptPairTensor, edge_attr: OptTensor)
      out = self.propagate(
          edge_index, x=x, edge_attr=edge_attr, size=size
      )
  
      x_r = x[1]
      if x_r is not None:
          out += (1 + self.eps) * x_r
  
      return self.nn(out)
  
imports: from typing import Callable, Union
  from torch_geometric.typing import OptPairTensor, Adj, OptTensor, Size
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch_sparse import SparseTensor, matmul
  from torch_geometric.nn.conv import MessagePassing
  from .inits import reset
label_desc: Use image node_id 3 for calling the GINEConv obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_attr, size) and returns: self