code: def load_subtensors(blocks, features):
      h_list = []
      for block in blocks:
          input_nodes = block.srcdata[dgl.NID]
          h_list.append(features[input_nodes])
      return h_list
  
imports: import dgl
  import numpy
  import argparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  from dgl.sampling import RandomWalkNeighborSampler
  from sklearn.metrics import f1_score
  from torch.utils.data import DataLoader
  from model_hetero import SemanticAttention
  from utils import EarlyStopping, set_random_seed
label_desc: Use image node_id 3 for calling a global function with example usage: load_subtensors(blocks, features) and returns: h_list