code: def replace_na(X, train_mask):
      if X.isna().any().any():
          return X.fillna(X.iloc[train_mask].min() - 1)
      return X
  
file_path: dgl/examples/pytorch/bgnn/run.py
imports: import json
  import os
  import numpy
  import pandas
  import torch
  import torch.nn.functional
  from BGNN import BGNNPredictor
  from category_encoders import CatBoostEncoder
  from dgl.data.utils import load_graphs
  from dgl.nn.pytorch import AGNNConv, APPNPConv, ChebConv, GATConv, GraphConv
  from sklearn import preprocessing
  from torch.nn import Dropout, ELU, Linear, ReLU, Sequential
label_desc: Use image node_id 3 for calling a global function with example usage: replace_na(X, train_mask) and returns: X, X