code: def parse_example_dataset(
      features, num_parallel_calls=1, deterministic=None
  ):
      """A transformation that parses `Example` protos into a `dict` of tensors.
  
      Parses a number of serialized `Example` protos given in `serialized`. We refer
      to `serialized` as a batch with `batch_size` many entries of individual
      `Example` protos.
  
      This op parses serialized examples into a dictionary mapping keys to `Tensor`,
      `SparseTensor`, and `RaggedTensor` objects. `features` is a dict from keys to
      `VarLenFeature`, `RaggedFeature`, `SparseFeature`, and `FixedLenFeature`
      objects. Each `VarLenFeature` and `SparseFeature` is mapped to a
      `SparseTensor`; each `RaggedFeature` is mapped to a `RaggedTensor`; and each
      `FixedLenFeature` is mapped to a `Tensor`. See `tf.io.parse_example` for more
      details about feature dictionaries.
  
      Args:
       features: A `dict` mapping feature keys to `FixedLenFeature`,
         `VarLenFeature`, `RaggedFeature`, and `SparseFeature` values.
       num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
          representing the number of parsing processes to call in parallel.
       deterministic: (Optional.) A boolean controlling whether determinism
          should be traded for performance by allowing elements to be produced out
          of order if some parsing calls complete faster than others. If
          `deterministic` is `None`, the
          `tf.data.Options.deterministic` dataset option (`True` by default) is used
          to decide whether to produce elements deterministically.
  
      Returns:
        A dataset transformation function, which can be passed to
        `tf.data.Dataset.apply`.
  
      Raises:
        ValueError: if features argument is None.
      """
      if features is None:
          raise ValueError(
              "Argument `features` is required, but not specified."
          )
  
      def _apply_fn(dataset):
          """Function from `Dataset` to `Dataset` that applies the transformation."""
          out_dataset = _ParseExampleDataset(
              dataset, features, num_parallel_calls, deterministic
          )
          if any(
              isinstance(feature, parsing_ops.SparseFeature)
              or isinstance(feature, parsing_ops.RaggedFeature)
              for feature in features.values()
          ):
              # pylint: disable=protected-access
              # pylint: disable=g-long-lambda
              out_dataset = out_dataset.map(
                  lambda x: parsing_ops._construct_tensors_for_composite_features(
                      features, x
                  ),
                  num_parallel_calls=num_parallel_calls,
              )
          return out_dataset
  
      return _apply_fn
  
file_path: tensorflow-upstream/tensorflow/python/data/experimental/ops/parsing_ops.py
imports: from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.util import structure
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import gen_experimental_dataset_ops
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: parse_example_dataset(features, num_parallel_calls, deterministic) and returns: _apply_fn, out_dataset