code: def test_load_balancing_with_dependencies(ray_start_cluster):
      # This test ensures that tasks are being assigned to all raylets in a
      # roughly equal manner even when the tasks have dependencies.
      cluster = ray_start_cluster
      num_nodes = 3
      for _ in range(num_nodes):
          cluster.add_node(num_cpus=1)
      ray.init(address=cluster.address)
  
      @ray.remote
      def f(x):
          time.sleep(0.1)
          return ray._private.worker.global_worker.node.unique_id
  
      # This object will be local to one of the raylets. Make sure
      # this doesn't prevent tasks from being scheduled on other raylets.
      x = ray.put(np.zeros(1000000))
  
      attempt_to_load_balance(f, [x], 100, num_nodes, 20)
  
imports: import collections
  import logging
  import platform
  import subprocess
  import sys
  import time
  import unittest
  import numpy
  import pytest
  import ray
  import ray.cluster_utils
  import ray.util.accelerators
  from ray._private.internal_api import memory_summary
  from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
  from ray._private.test_utils import Semaphore, SignalActor, object_memory_usage, get_metric_check_condition, wait_for_condition, MetricSamplePattern
label_desc: Use image node_id 6 for calling a global function with example usage: test_load_balancing_with_dependencies(ray_start_cluster) and returns: ray