code: def train_program(
      self,
  ):
      label = F.data(name="label", shape=[None, 1], dtype="int64")
      train_idx = F.data(name="train_idx", shape=[None], dtype="int64")
      prediction = L.gather(self.out_feat, train_idx, overwrite=False)
      label = L.gather(label, train_idx, overwrite=False)
      cost = L.softmax_with_cross_entropy(
          logits=prediction, label=label
      )
      avg_cost = L.mean(cost)
      self.avg_cost = avg_cost
  
file_path: PGL/ogb_examples/nodeproppred/ogbn-arxiv/unimp_appnp_vnode_smooth/model.py
imports: import math
  import pgl
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.utils import paddle_helper
  from module.model_unimp import graph_transformer, linear, attn_appnp
label_desc: Use image node_id 4 for calling the Arxiv_baseline_model obj's underlying member method code with example usage: obj.train_program() without return types