class_docstr: GIN model
class_docstr_tok: ["GIN","model"]
code: def forward(self, g, h):
      hidden_rep = [h]
  
      for i in range(self.num_layers - 1):
          h = self.ginlayers[i](g, h)
          h = self.batch_norms[i](h)
          h = nd.relu(h)
          hidden_rep.append(h)
  
      score_over_layer = 0
      # perform pooling over all nodes in each graph in every layer
      for i, h in enumerate(hidden_rep):
          pooled_h = self.pool(g, h)
          score_over_layer = score_over_layer + self.drop(
              self.linears_prediction[i](pooled_h)
          )
  
      return score_over_layer
  
file_path: dgl/examples/mxnet/gin/gin.py
imports: import mxnet
  from dgl.nn.mxnet.conv import GINConv
  from dgl.nn.mxnet.glob import AvgPooling, MaxPooling, SumPooling
  from mxnet import gluon, nd
  from mxnet.gluon import nn
label_desc: Use image node_id 2 for calling the GIN obj's underlying member method code with example usage: obj.forward(g, h) and returns: score_over_layer