class_docstr: Grahpsage network that concatenate several graphsage layer
class_docstr_tok: ["Grahpsage","network","that","concatenate","several","graphsage","layer"]
code: def forward(self, g, features):
      h = features
      for layer in self.layers:
          h = layer(g, h)
      return h
  
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 2 for calling the GraphSage obj's underlying member method code with example usage: obj.forward(g, features) and returns: h