code: def test_tl_from_list_of_tensors_different_shapes():
      shapes = [
          (1, 2, 3),
          (4, 5, 6),
          (128, 128, 128),
          (8, 8, 8),
          (13, 47, 131),
      ]
      for size in [10, 5, 36, 1]:
          np_arrays = [
              np.random.rand(*shapes[i])
              for i in np.random.choice(range(len(shapes)), size=size)
          ]
  
          tl_cpu = TensorListCPU([TensorCPU(a) for a in np_arrays])
          tl_gpu = TensorListGPU(
              [TensorCPU(a)._as_gpu() for a in np_arrays]
          )
  
          for arr, tensor_cpu, tensor_gpu in zip(
              np_arrays, tl_cpu, tl_gpu
          ):
              np.testing.assert_array_equal(arr, tensor_cpu)
              np.testing.assert_array_equal(arr, tensor_gpu.as_cpu())
  
imports: import numpy
  import nvidia.dali.fn
  import nvidia.dali.types
  import warnings
  from numpy.testing import assert_array_equal
  from nvidia.dali import pipeline_def
  from nvidia.dali.backend_impl import TensorCPU, TensorListCPU, TensorListGPU
  from nvidia.dali.backend_impl import types
  import nvidia.dali
  from nose_utils import assert_raises
  from nose import SkipTest
  from test_utils import dali_type_to_np, py_buffer_from_address, get_device_memory_info
label_desc: Use image node_id 20 for calling a global function with example usage: test_tl_from_list_of_tensors_different_shapes() without return types