code: def __init__(self, in_channels, out_channels, bias=True):
      super(LinearConv, self).__init__()
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.linear = torch.nn.Linear(in_channels, out_channels, bias)
  
imports: from torch_geometric.nn import GATConv, GCNConv, ChebConv, SAGEConv, GatedGraphConv, ARMAConv, SGConv
  import torch_geometric.nn
  import torch
  from torch import nn
  import torch.nn.functional
  import inspect
  import sys
  from torch_scatter import scatter_add
  import torch_scatter
  from torch.nn import Parameter
  from torch_geometric.nn.inits import glorot, zeros
  from torch_geometric.utils import remove_self_loops, add_self_loops, add_remaining_self_loops, softmax
label_desc: Use image node_id 1 to create a new LinearConv object from inherited base classes: nn with example: obj = LinearConv(in_channels, out_channels, bias)