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(PreemphasisPipeline, self).__init__(
          batch_size, num_threads, device_id, seed=SEED
      )
      self.device = device
      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:
          self.preemph = ops.PreemphasisFilter(
              device=device, border=border
          )
      else:
          self.preemph = ops.PreemphasisFilter(
              device=device, border=border, preemph_coeff=preemph_coeff
          )
  
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 PreemphasisPipeline object from inherited base classes: Pipeline with example: obj = PreemphasisPipeline(device, batch_size, iterator, border, preemph_coeff, per_sample_coeff, num_threads, device_id)