code: def __init__(
      self,
      device,
      batch_size,
      iterator,
      border="clamp",
      preemph_coeff=0.97,
      per_sample_coeff=False,
      num_threads=4,
      device_id=0,
  ):
      super(PreemphasisPythonPipeline, self).__init__(
          batch_size,
          num_threads,
          device_id,
          seed=SEED,
          exec_async=False,
          exec_pipelined=False,
      )
      self.device = "cpu"
      self.iterator = iterator
      self.per_sample_coeff = per_sample_coeff
      self.uniform = ops.random.Uniform(range=(0.5, 0.97), seed=1234)
      if self.per_sample_coeff:
          function = partial(preemph_func, border)
      else:
          function = partial(preemph_func, border, preemph_coeff)
      self.preemph = ops.PythonFunction(function=function)
  
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 1 to create a new PreemphasisPythonPipeline object from inherited base classes: Pipeline with example: obj = PreemphasisPythonPipeline(device, batch_size, iterator, border, preemph_coeff, per_sample_coeff, num_threads, device_id)