class_docstr: Waymo Open Dataset.
class_docstr_tok: ["Waymo","Open","Dataset","."]
docstr: Generate examples as dicts.
  
  Args:
    pipeline: Apache Beam pipeline.
    tf_record_files: .tfrecord files.
  
  Returns:
    Dict of examples.
docstr_tok: ["Generate","examples","as","dicts",".","Args",":","pipeline",":","Apache","Beam","pipeline",".","tf_record_files",":",".tfrecord","files",".","Returns",":","Dict","of","examples","."]
code: def _build_pcollection(self, pipeline, tf_record_files):
      """Generate examples as dicts.
  
      Args:
        pipeline: Apache Beam pipeline.
        tf_record_files: .tfrecord files.
  
      Returns:
        Dict of examples.
      """
      beam = tfds.core.lazy_imports.apache_beam
  
      def _process_example(tf_record_file):
          for image_and_annotation in _generate_images_and_annotations(
              tf_record_file
          ):
              key = "%s:%s" % (
                  image_and_annotation["context"]["name"],
                  image_and_annotation["timestamp_micros"],
              )
              yield key, image_and_annotation
  
      return (
          pipeline
          | beam.Create(tf_record_files)
          | beam.FlatMap(_process_example)
      )
  
file_path: datasets/tensorflow_datasets/object_detection/waymo_open_dataset.py
imports: from __future__ import annotations
  import os
  from absl import logging
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  from tensorflow_datasets.proto import waymo_dataset_pb2
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the WaymoOpenDataset obj's underlying member method code with example usage: obj._build_pcollection(pipeline, tf_record_files) and returns: unknown