code: def __init__(
      self,
      cmn_op,
      device,
      batch_size,
      num_threads=1,
      device_id=0,
      num_gpus=1,
      dtype=types.FLOAT,
      output_layout="HWC",
      mirror_probability=0.0,
      mean=[0.0, 0.0, 0.0],
      std=[1.0, 1.0, 1.0],
      scale=None,
      shift=None,
      pad_output=False,
  ):
      super().__init__(batch_size, num_threads, device_id, seed=7865)
      self.device = device
      self.input = ops.readers.Caffe(
          path=caffe_db_folder, shard_id=device_id, num_shards=num_gpus
      )
      self.decode = ops.decoders.Image(
          device="cpu", output_type=types.RGB
      )
      self.cmn = cmn_op(
          device=self.device,
          dtype=dtype,
          output_layout=output_layout,
          crop=(224, 224),
          crop_pos_x=0.3,
          crop_pos_y=0.2,
          mean=mean,
          std=std,
          scale=scale,
          shift=shift,
          pad_output=pad_output,
      )
      self.coin = ops.random.CoinFlip(
          probability=mirror_probability, seed=7865
      )
  
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 1 to create a new CropMirrorNormalizePipeline object from inherited base classes: Pipeline with example: obj = CropMirrorNormalizePipeline(cmn_op, device, batch_size, num_threads, device_id, num_gpus, dtype, output_layout, mirror_probability, mean, std, scale, shift, pad_output)