code: def __init__(
      self,
      filenames,
      idx_filenames,
      height,
      width,
      batch_size,
      num_threads,
      dtype=tf.uint8,
      dali_cpu=True,
      deterministic=False,
      training=False,
  ):
      device_id = hvd.local_rank()
      shard_id = hvd.rank()
      num_gpus = hvd.size()
      self.pipe = get_dali_pipeline(
          tfrec_filenames=filenames,
          tfrec_idx_filenames=idx_filenames,
          height=height,
          width=width,
          batch_size=batch_size,
          num_threads=num_threads,
          device_id=device_id,
          shard_id=shard_id,
          num_gpus=num_gpus,
          dali_cpu=dali_cpu,
          training=training,
          seed=7 * (1 + hvd.rank()) if deterministic else None,
      )
  
      self.daliop = dali_tf.DALIIterator()
  
      self.batch_size = batch_size
      self.height = height
      self.width = width
      self.device_id = device_id
  
      self.dalidataset = dali_tf.DALIDataset(
          pipeline=self.pipe,
          output_shapes=((batch_size, height, width, 3), (batch_size)),
          batch_size=batch_size,
          output_dtypes=(tf.float32, tf.int64),
          device_id=device_id,
      )
  
imports: import tensorflow
  import sys
  import os
  import numpy
  from subprocess import call
  import horovod.tensorflow.keras
  from nvidia.dali.pipeline import pipeline_def
  import nvidia.dali.fn
  import nvidia.dali.types
  import nvidia.dali.tfrecord
label_desc: Use image node_id 1 to create a new DALIPreprocessor object from inherited base classes: object with example: obj = DALIPreprocessor(filenames, idx_filenames, height, width, batch_size, num_threads, dtype, dali_cpu, deterministic, training)