code: def __init__(self, args):
      super(TFRecordDetectionPipeline, self).__init__(
          args.batch_size, args.num_workers, 0, 0
      )
      self.input = ops.readers.TFRecord(
          path=os.path.join(
              test_dummy_data_path, "small_coco.tfrecord"
          ),
          index_path=os.path.join(
              test_dummy_data_path, "small_coco_index.idx"
          ),
          features={
              "image/encoded": tfrec.FixedLenFeature(
                  (), tfrec.string, ""
              ),
              "image/object/class/label": tfrec.VarLenFeature(
                  [], tfrec.int64, 0
              ),
              "image/object/bbox": tfrec.VarLenFeature(
                  [4], tfrec.float32, 0.0
              ),
          },
          shard_id=0,
          num_shards=1,
          random_shuffle=False,
      )
  
      self.decode_gpu = ops.decoders.Image(
          device="mixed", output_type=types.RGB
      )
      self.cast = ops.Cast(dtype=types.INT32)
      self.box_encoder = ops.BoxEncoder(
          device="cpu", criteria=0.5, anchors=coco_anchors()
      )
  
file_path: DALI/dali/test/python/test_coco_tfrecord.py
imports: import argparse
  import os
  import nvidia.dali.ops
  import nvidia.dali.types
  from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.tfrecord
  from test_detection_pipeline import coco_anchors
  from test_utils import compare_pipelines, get_dali_extra_path
label_desc: Use image node_id 1 to create a new TFRecordDetectionPipeline object from inherited base classes: Pipeline with example: obj = TFRecordDetectionPipeline(args)