code: def test_render_multi_thread_multi_gpu(self):
      # Multiple threads using up multiple GPUs; more threads than GPUs.
      # This is certainly not encouraged in practice, but shouldn't fail. Note that
      # the context store will only allow one rendering at a time to occur on a
      # single GPU, even across threads.
      n_gpus = torch.cuda.device_count()
      n_threads = 10
      kwargs = {
          "r": 1.0,
          "g": 0.0,
          "b": 0.0,
          "verbose": True,
      }
  
      threads = []
      for thread_id in range(n_threads):
          kwargs.update(
              {
                  "cuda_device_id": thread_id % n_gpus,
                  "thread_id": thread_id,
              }
          )
          threads.append(
              threading.Thread(
                  target=_draw_squares_with_context_store,
                  kwargs=dict(kwargs),
              )
          )
  
      for thread in threads:
          thread.start()
      for thread in threads:
          thread.join()
  
imports: import ctypes
  import os
  import sys
  import threading
  import unittest
  import torch
  import pycuda._driver
  from OpenGL import GL
  from OpenGL.raw.EGL._errors import EGLError
  from pytorch3d.renderer.opengl import _can_import_egl_and_pycuda
  from pytorch3d.renderer.opengl.opengl_utils import _define_egl_extension, _egl_convert_to_int_array, _get_cuda_device, egl, EGLContext, global_device_context_store
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 9 for calling the TestOpenGLMultiThreaded obj's underlying member method code with example usage: obj.test_render_multi_thread_multi_gpu() without return types