class_docstr: GraphSage layer in Inductive learning paper by hamilton
  Here, graphsage layer is a reduced function in DGL framework
class_docstr_tok: ["GraphSage","layer","in","Inductive","learning","paper","by","hamilton","Here",",","graphsage","layer","is","a","reduced","function","in","DGL","framework"]
code: def __init__(
      self,
      in_feats,
      out_feats,
      activation,
      dropout,
      aggregator_type,
      bn=False,
      bias=True,
  ):
      super(GraphSageLayer, self).__init__()
      self.use_bn = bn
      self.bundler = Bundler(
          in_feats, out_feats, activation, dropout, bias=bias
      )
      self.dropout = nn.Dropout(p=dropout)
  
      if aggregator_type == "maxpool":
          self.aggregator = MaxPoolAggregator(
              in_feats, in_feats, activation, bias
          )
      elif aggregator_type == "lstm":
          self.aggregator = LSTMAggregator(in_feats, in_feats)
      else:
          self.aggregator = MeanAggregator()
  
imports: import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from scipy.linalg import block_diag
  from model.loss import EntropyLoss
  from ..model_utils import masked_softmax
  from .aggregator import LSTMAggregator, MaxPoolAggregator, MeanAggregator
  from .bundler import Bundler
label_desc: Use image node_id 1 to create a new GraphSageLayer object from inherited base classes: nn with example: obj = GraphSageLayer(in_feats, out_feats, activation, dropout, aggregator_type, bn, bias)