code: def __init__(
      self,
      in_channels: Union[int, Tuple[int, int]],
      out_channels: int,
      heads: int = 1,
      concat: bool = False,
      negative_slope: float = 0.2,
      dropout: float = 0.0,
      add_self_loops: bool = True,
      bias: bool = True,
      **kwargs,
  ):
      super(GATConv, self).__init__(aggr="add", node_dim=0, **kwargs)
  
      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.add_self_loops = add_self_loops
  
      self.lin = Linear(in_channels, heads * out_channels, bias=False)
  
      self.att = Parameter(torch.Tensor(1, heads, 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)
  
      self.reset_parameters()
  
imports: from torch_geometric.nn import MessagePassing
  from torch_geometric.utils import softmax
  import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import GCNConv, SAGEConv, GATConv, ARMAConv, ChebConv, GatedGraphConv, SGConv
  from typing import Union, Tuple, Optional
  from torch_geometric.typing import OptPairTensor, Adj, Size, NoneType, OptTensor
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import Parameter, Linear
  from torch_sparse import SparseTensor, set_diag
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.utils import remove_self_loops, add_self_loops, softmax
  from torch_geometric.nn.inits import glorot, zeros
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.functional
  from torch.nn import Parameter
  from torch_geometric.nn.inits import glorot, zeros
  from torch_geometric.utils import softmax
  from torch_scatter import scatter_add
  import numpy
  from ..graph_nas_macro import GeoLayer
label_desc: Use image node_id 1 to create a new GATConv2 object from inherited base classes: MessagePassing with example: obj = GATConv2(in_channels, out_channels, heads, concat, negative_slope, dropout, add__loops, bias)