class_docstr: Parser for Example in Example format.
class_docstr_tok: ["Parser","for","Example","in","Example","format","."]
docstr: Decodes into serialized context and examples.
docstr_tok: ["Decodes","into","serialized","context","and","examples","."]
code: def _decode_as_serialized_example_list(self, serialized):
      """Decodes into serialized context and examples."""
      feature_spec = {
          "serialized_context": tf.io.FixedLenFeature([1], tf.string),
          "serialized_examples": tf.io.VarLenFeature(tf.string),
      }
      features = tf.compat.v1.io.parse_example(serialized, feature_spec)
      # Generate sizes from `serialized_examples`.
      lists = features["serialized_examples"]
      mask = tf.scatter_nd(
          indices=lists.indices,
          updates=tf.ones_like(lists.values, dtype=tf.int32),
          shape=lists.dense_shape,
      )
      sizes = tf.reduce_sum(input_tensor=mask, axis=1)
      return (
          features["serialized_context"],
          tf.sparse.to_dense(lists, default_value=""),
          sizes,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import functools
  import six
  import tensorflow
  from tensorflow import estimator
  from google.protobuf import descriptor_pb2
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 1 for calling the _ExampleInExampleParser obj's underlying member method code with example usage: obj._decode_as_serialized_example_list(serialized) and returns: features,  tf,  sizes