code: def test_object_transfer_during_oom(ray_start_cluster_head):
      cluster = ray_start_cluster_head
      cluster.add_node(object_store_memory=75 * 1024 * 1024)
  
      @ray.remote
      def put():
          return np.random.rand(5 * 1024 * 1024)  # 40 MB data
  
      _ = ray.put(np.random.rand(5 * 1024 * 1024))
      remote_ref = put.remote()
  
      # Getting the remote ref is possible even though we don't have enough
      # memory locally to hold both objects once.
      ray.get(remote_ref)
  
imports: import multiprocessing
  import time
  import warnings
  from collections import defaultdict
  import numpy
  import pytest
  import ray
  from ray.cluster_utils import Cluster, cluster_not_supported
  from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy
label_desc: Use image node_id 3 for calling a global function with example usage: test_object_transfer_during_oom(ray_start_cluster_head) and returns: np