original_docstring: 
      Parses a single serialized example.
  
      :param example_proto: Serialized example as a binary string.
      :type example_proto: tensorflow.string
      :param data_shape: Original shape of the example data.
      :type data_shape: tuple[int]
      :param data_type: Type of each data point in the example or the example itself if it is bytes or str.
          Optional, default: np.float32
      :type data_type: type
      :param label_type: Type of the label (if any). If there is no label, this must be specified by setting this
          parameter to None. Optional, default: int.
      :type label_type: type
      :return: Dictionary with 'data' and 'example_id' fields and an optional 'label' field.
      :rtype: tuple[tensorflow.Tensor]
      
docstring: Parses a single serialized example.
docstring_tokens: Parses a single serialized example .
code: def _parse_example(example_proto, data_shape, data_type=np.float32, label_type=int):
      """
      Parses a single serialized example.
  
      :param example_proto: Serialized example as a binary string.
      :type example_proto: tensorflow.string
      :param data_shape: Original shape of the example data.
      :type data_shape: tuple[int]
      :param data_type: Type of each data point in the example or the example itself if it is bytes or str.
          Optional, default: np.float32
      :type data_type: type
      :param label_type: Type of the label (if any). If there is no label, this must be specified by setting this
          parameter to None. Optional, default: int.
      :type label_type: type
      :return: Dictionary with 'data' and 'example_id' fields and an optional 'label' field.
      :rtype: tuple[tensorflow.Tensor]
      """
      # Basic example description with data field and ID.
      feature_description = {
          'data': tf.io.FixedLenFeature(data_shape, _auto_tf_type(data_type)),
          'example_id': tf.io.FixedLenFeature([], _auto_tf_type(str))
      }
      # Add the label field if a label type is specified.
      if label_type is not None:
          feature_description['label'] = tf.io.FixedLenFeature([], _auto_tf_type(label_type))
  
      # Parse the example.
      example = tf.io.parse_example(example_proto, feature_description)
  
      return example
code_tokens: def _parse_example ( example_proto , data_shape , data_type = np . float32 , label_type = int ) : feature_description = { 'data' : tf . io . FixedLenFeature ( data_shape , _auto_tf_type ( data_type ) ) , 'example_id' : tf . io . FixedLenFeature ( [ ] , _auto_tf_type ( str ) ) } if label_type is not None : feature_description [ 'label' ] = tf . io . FixedLenFeature ( [ ] , _auto_tf_type ( label_type ) ) example = tf . io . parse_example ( example_proto , feature_description ) return example
short_docstring: Parses a single serialized example.
short_docstring_tokens: Parses a single serialized example .
comment: """
      Parses a single serialized example.
  
      :param example_proto: Serialized example as a binary string.
      :type example_proto: tensorflow.string
      :param data_shape: Original shape of the example data.
      :type data_shape: tuple[int]
      :param data_type: Type of each data point in the example or the example itself if it is bytes or str.
          Optional, default: np.float32
      :type data_type: type
      :param label_type: Type of the label (if any). If there is no label, this must be specified by setting this
          parameter to None. Optional, default: int.
      :type label_type: type
      :return: Dictionary with 'data' and 'example_id' fields and an optional 'label' field.
      :rtype: tuple[tensorflow.Tensor]
      """$SEP$# Basic example description with data field and ID.$SEP$# Add the label field if a label type is specified.$SEP$# Parse the example.