code: def reset_parameters(self):
      gain = nn.init.calculate_gain("relu")
      if hasattr(self, "fc"):
          nn.init.xavier_normal_(self.fc.weight, gain=gain)
      else:
          nn.init.xavier_normal_(self.fc_src.weight, gain=gain)
          nn.init.xavier_normal_(self.fc_dst.weight, gain=gain)
      nn.init.xavier_normal_(self.attn_l, gain=gain)
      if isinstance(self.attn_r, nn.Parameter):
          nn.init.xavier_normal_(self.attn_r, gain=gain)
      if isinstance(self.res_fc, nn.Linear):
          nn.init.xavier_normal_(self.res_fc.weight, gain=gain)
  
imports: import dgl.nn.pytorch
  import torch
  import torch.nn
  from dgl import function
  from dgl.ops import edge_softmax
  from dgl.utils import expand_as_pair
label_desc: Use image node_id 2 for calling the GATConv obj's underlying member method code with example usage: obj.reset_parameters() without return types