code: def __init__(
      self, infeat, outfeat, use_bn=True, mean=False, add_self=False
  ):
      super().__init__()
      self.add_self = add_self
      self.use_bn = use_bn
      self.mean = mean
      self.W = nn.Linear(infeat, outfeat, bias=True)
  
      nn.init.xavier_uniform_(
          self.W.weight, gain=nn.init.calculate_gain("relu")
      )
  
file_path: dgl/examples/pytorch/diffpool/model/tensorized_layers/graphsage.py
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new BatchedGraphSAGE object from inherited base classes: nn with example: obj = BatchedGraphSAGE(infeat, outfeat, use_bn, mean, add_)