class_docstr: Maxpooling aggregator for graphsage
class_docstr_tok: ["Maxpooling","aggregator","for","graphsage"]
code: def __init__(self, in_feats, out_feats, activation, bias):
      super(MaxPoolAggregator, self).__init__()
      self.linear = nn.Linear(in_feats, out_feats, bias=bias)
      self.activation = activation
      # Xavier initialization of weight
      nn.init.xavier_uniform_(
          self.linear.weight, gain=nn.init.calculate_gain("relu")
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new MaxPoolAggregator object from inherited base classes: Aggregator with example: obj = MaxPoolAggregator(in_feats, out_feats, activation, bias)