code: def __init__(self, input_dims, hidden_dims, output_dims, act=F.relu):
      super().__init__()
  
      self.act = act
      self.Q = nn.Linear(input_dims, hidden_dims)
      self.W = nn.Linear(input_dims + hidden_dims, output_dims)
      self.reset_parameters()
      self.dropout = nn.Dropout(0.5)
  
imports: import argparse
  import pickle
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, IterableDataset
  from ..None import utils
label_desc: Use image node_id 1 to create a new WeightedSAGEConv object from inherited base classes: nn with example: obj = WeightedSAGEConv(input_dims, hidden_dims, output_dims, act)