code: def to_on_disk_tensor(test_dir, name, t):
      path = os.path.join(test_dir, name + ".npy")
      t = t.numpy()
      np.save(path, t)
      # The Pytorch tensor is a view of the numpy array on disk, which does not
      # consume memory.
      t = torch.as_tensor(np.load(path, mmap_mode="r+"))
      return t
  
imports: import os
  import tempfile
  import unittest
  from functools import reduce
  from operator import mul
  import backend
  import numpy
  import pydantic
  import pytest
  import torch
  from dgl import graphbolt
label_desc: Use image node_id 1 for calling a global function with example usage: to_on_disk_tensor(test_dir, name, t) and returns: t