code: def check_cmn_crop_sequence_length(
      cmn_op,
      device,
      batch_size,
      dtype,
      input_layout,
      input_shape,
      output_layout,
      mirror_probability,
      mean,
      std,
      should_pad,
  ):
      crop_d, crop_h, crop_w = (8, 16, 32)
      eii1 = RandomDataIterator(batch_size, shape=input_shape)
  
      pipe = CMNRandomDataPipeline(
          cmn_op,
          device,
          batch_size,
          input_layout,
          iter(eii1),
          dtype=dtype,
          output_layout=output_layout,
          mirror_probability=mirror_probability,
          mean=mean,
          std=std,
          pad_output=should_pad,
          crop_seq_as_depth=True,
      )
      pipe.build()
      out = pipe.run()
      out_data = out[0]
  
      expected_out_shape = (
          (crop_d, 3, crop_h, crop_w)
          if output_layout == "FCHW"
          else (crop_d, crop_h, crop_w, 3)
      )
  
      for i in range(batch_size):
          sh = as_array(out_data[i]).shape
          assert (
              sh == expected_out_shape
          ), "Shape mismatch {} != {}".format(sh, expected_out_shape)
  
imports: import numpy
  import nvidia.dali
  import nvidia.dali.fn
  import nvidia.dali.ops
  import nvidia.dali.types
  import os
  from functools import partial
  from nvidia.dali import pipeline_def
  from nvidia.dali.pipeline import Pipeline
  from nose2.tools import params
  from nose_utils import assert_raises
  from nose_utils import raises
  from test_slice import check_slice_output, abs_slice_start_and_end
  from test_utils import RandomDataIterator
  from test_utils import as_array
  from test_utils import compare_pipelines, dali_type_to_np
  from test_utils import get_dali_extra_path
  import itertools
label_desc: Use image node_id 9 for calling a global function with example usage: check_cmn_crop_sequence_length(cmn_op, device, batch_size, dtype, input_layout, input_shape, output_layout, mirror_probability, mean, std, should_pad) without return types