code: def __init__(
      self,
      in_channels,
      out_channels,
      heads=1,
      concat=True,
      negative_slope=0.2,
      dropout=0,
      bias=True,
      att_type="gat",
      agg_type="sum",
      pool_dim=0,
  ):
      if agg_type in ["sum", "mlp"]:
          super(GeoLayer, self).__init__("add")
      elif agg_type in ["mean", "max"]:
          super(GeoLayer, self).__init__(agg_type)
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.heads = heads
      self.concat = concat
      self.negative_slope = negative_slope
      self.dropout = dropout
      self.att_type = att_type
      self.agg_type = agg_type
  
      # GCN weight
      self.gcn_weight = None
  
      self.weight = Parameter(
          torch.Tensor(in_channels, heads * out_channels)
      )
      self.att = Parameter(torch.Tensor(1, heads, 2 * out_channels))
  
      if bias and concat:
          self.bias = Parameter(torch.Tensor(heads * out_channels))
      elif bias and not concat:
          self.bias = Parameter(torch.Tensor(out_channels))
      else:
          self.register_parameter("bias", None)
  
      if self.att_type in ["generalized_linear"]:
          self.general_att_layer = torch.nn.Linear(
              out_channels, 1, bias=False
          )
  
      if self.agg_type in ["mean", "max", "mlp"]:
          if pool_dim <= 0:
              pool_dim = 128
      self.pool_dim = pool_dim
      if pool_dim != 0:
          self.pool_layer = torch.nn.ModuleList()
          self.pool_layer.append(
              torch.nn.Linear(self.out_channels, self.pool_dim)
          )
          self.pool_layer.append(
              torch.nn.Linear(self.pool_dim, self.out_channels)
          )
      else:
          pass
      self.reset_parameters()
  
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 GeoLayer object from inherited base classes: MessagePassing with example: obj = GeoLayer(in_channels, out_channels, heads, concat, negative_slope, dropout, bias, att_type, agg_type, pool_dim)