code: def __init__(
      self,
      batch_size,
      num_threads,
      device_id,
      pipelined=True,
      exec_async=True,
  ):
      super(HybridPipe, self).__init__(
          batch_size,
          num_threads,
          device_id,
          exec_pipelined=pipelined,
          exec_async=exec_async,
      )
      self.input = ops.ExternalSource()
      self.decode = ops.ImageDecoder(
          device="mixed", output_type=types.RGB
      )
      self.resize = ops.Resize(
          device="gpu", interp_type=types.INTERP_LINEAR
      )
      self.cmnp = ops.CropMirrorNormalize(
          device="gpu",
          dtype=types.FLOAT16,
          crop=(224, 224),
          mean=[128.0, 128.0, 128.0],
          std=[1.0, 1.0, 1.0],
      )
      self.uniform = ops.random.Uniform(range=(0.0, 1.0))
      self.resize_uniform = ops.random.Uniform(range=(256.0, 480.0))
      self.mirror = ops.random.CoinFlip(probability=0.5)
  
imports: import argparse
  from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  import nvidia.dali.types
  import numpy
  from timeit import default_timer
label_desc: Use image node_id 1 to create a new HybridPipe object from inherited base classes: Pipeline with example: obj = HybridPipe(batch_size, num_threads, device_id, pipelined, exec_async)