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,
  ):
      super(GeoLayer, self).__init__()
      if agg_type in ["sum", "mlp"]:
          agg_type = "add"
      elif agg_type in ["mean", "max"]:
          pass
      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
      self.reduce_func = {
          "add": fn.sum,
          "mean": fn.mean,
          "max": fn.max,
      }.get(self.agg_type, None)
  
      # 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: import torch
  from torch import nn
  import torch.nn.functional
  import dgl
  from dgl.nn.pytorch.conv import *
  import dgl.function
  import math
  from dgl.utils import expand_as_pair
  from dgl.nn.functional import edge_softmax
  from torch.nn import Parameter
  import torch
  from torch import Tensor
  from ..scatter_utils import *
  from typing import Optional, Tuple, Union
label_desc: Use image node_id 1 to create a new GeoLayer object from inherited base classes: nn with example: obj = GeoLayer(in_channels, out_channels, heads, concat, negative_slope, dropout, bias, att_type, agg_type, pool_dim)