class_docstr: Starcraft video datasets.
class_docstr_tok: ["Starcraft","video","datasets","."]
docstr: Parses single video from the input tfrecords.
  
  Args:
    example_proto: tfExample proto with a single video.
  
  Returns:
    dict with all frames, positions and actions.
docstr_tok: ["Parses","single","video","from","the","input","tfrecords",".","Args",":","example_proto",":","tfExample","proto","with","a","single","video",".","Returns",":","dict","with","all","frames",",","positions","and","actions","."]
code: def _parse_single_video(self, example_proto):
      """Parses single video from the input tfrecords.
  
      Args:
        example_proto: tfExample proto with a single video.
  
      Returns:
        dict with all frames, positions and actions.
      """
      context_features = {
          "game_duration_loops": tf.io.FixedLenFeature([1], tf.int64),
          "game_duration_seconds": tf.io.FixedLenFeature(
              [1], tf.float32
          ),
          "n_steps": tf.io.FixedLenFeature([1], tf.int64),
          "screen_size": tf.io.FixedLenFeature([2], tf.int64),
      }
  
      sequence_features = {
          "rgb_screen": tf.io.FixedLenSequenceFeature([], tf.string),
      }
  
      _, seq_feat = tf.io.parse_single_sequence_example(
          example_proto,
          context_features=context_features,
          sequence_features=sequence_features,
      )
  
      video_frames = tf.map_fn(
          tf.image.decode_png, seq_feat["rgb_screen"], dtype=tf.uint8
      )
      return video_frames
  
file_path: datasets/tensorflow_datasets/datasets/starcraft_video/starcraft_video_dataset_builder.py
imports: from __future__ import annotations
  import os
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the Builder obj's underlying member method code with example usage: obj._parse_single_video(example_proto) and returns: video_frames