code: def numba_func_split_image_pipe(
      run_fn=None,
      out_types=None,
      in_types=None,
      outs_ndim=None,
      ins_ndim=None,
      setup_fn=None,
      batch_processing=None,
      device="cpu",
      blocks=None,
      threads_per_block=None,
  ):
      files, _ = dali.fn.readers.caffe(path=lmdb_folder)
      dec_device = "cpu" if device == "cpu" else "mixed"
      images_in = dali.fn.decoders.image(files, device=dec_device)
      out0, out1, out2 = numba_function(
          images_in,
          run_fn=run_fn,
          out_types=out_types,
          in_types=in_types,
          outs_ndim=outs_ndim,
          ins_ndim=ins_ndim,
          setup_fn=setup_fn,
          batch_processing=batch_processing,
          device=device,
          blocks=blocks,
          threads_per_block=threads_per_block,
      )
      return images_in, out0, out1, out2
  
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 34 for calling a global function with example usage: numba_func_split_image_pipe(run_fn, out_types, in_types, outs_ndim, ins_ndim, setup_fn, batch_processing, device, blocks, threads_per_block) and returns: images_in,  out0,  out1,  out2