code: def check_preemphasis_operator(
      device, batch_size, border, preemph_coeff, per_sample_coeff
  ):
      eii1 = RandomlyShapedDataIterator(
          batch_size,
          min_shape=(100,),
          max_shape=(10000,),
          dtype=np.float32,
      )
      eii2 = RandomlyShapedDataIterator(
          batch_size,
          min_shape=(100,),
          max_shape=(10000,),
          dtype=np.float32,
      )
      compare_pipelines(
          PreemphasisPipeline(
              device,
              batch_size,
              iter(eii1),
              border=border,
              preemph_coeff=preemph_coeff,
              per_sample_coeff=per_sample_coeff,
          ),
          PreemphasisPythonPipeline(
              device,
              batch_size,
              iter(eii2),
              border=border,
              preemph_coeff=preemph_coeff,
              per_sample_coeff=per_sample_coeff,
          ),
          batch_size=batch_size,
          N_iterations=3,
      )
  
file_path: DALI/dali/test/python/operator_2/test_preemph.py
imports: from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  import nvidia.dali.fn
  import numpy
  from functools import partial
  from test_utils import compare_pipelines
  from test_utils import RandomlyShapedDataIterator
label_desc: Use image node_id 2 for calling a global function with example usage: check_preemphasis_operator(device, batch_size, border, preemph_coeff, per_sample_coeff) without return types