code: def test_multiple_ins_gpu():
      blocks = [32, 32, 1]
      threads_per_block = [32, 8, 1]
      pipe = numba_multiple_ins_pipe(
          shapes=[(10, 10)],
          dtype=np.uint8,
          batch_size=8,
          num_threads=1,
          device_id=0,
          run_fn=multiple_ins_run_gpu,
          setup_fn=multiple_ins_setup,
          out_types=[dali_types.UINT8],
          in_types=[dali_types.UINT8 for i in range(3)],
          outs_ndim=[3],
          ins_ndim=[2, 2, 2],
          device="gpu",
          blocks=blocks,
          threads_per_block=threads_per_block,
      )
      pipe.build()
      for _ in range(3):
          outs = pipe.run()
          out_arr = to_array(outs[0][0])
          assert np.array_equal(
              out_arr, np.zeros((10, 10, 3), dtype=np.uint8)
          )
  
imports: import numpy
  import os
  from nvidia.dali import pipeline_def
  import nvidia.dali
  import nvidia.dali.fn
  import nvidia.dali.types
  from nose import with_setup
  from test_utils import get_dali_extra_path, to_array, check_numba_compatibility_cpu, check_numba_compatibility_gpu
  from nvidia.dali.plugin.numba.fn.experimental import numba_function
  from numba import cuda
label_desc: Use image node_id 42 for calling a global function with example usage: test_multiple_ins_gpu() without return types