class_docstr: View of a single feature.
  
  This class provides accessor methods, as well as access to the underlying
  proto. Where possible, accessors should be used in place of proto access (for
  example, x.numeric_statistics() instead of x.proto().num_stats) in order to
  support future extension of the proto.
class_docstr_tok: ["View","of","a","single","feature",".","This","class","provides","accessor","methods",",","as","well","as","access","to","the","underlying","proto",".","Where","possible",",","accessors","should","be","used","in","place","of","proto","access","(","for","example",",","x.numeric_statistics","(",")","instead","of","x.proto","(",")",".num_stats",")","in","order","to","support","future","extension","of","the","proto","."]
docstr: Retrieve byte statistics if available.
docstr_tok: ["Retrieve","byte","statistics","if","available","."]
code: def bytes_statistics(
      self,
  ) -> Optional[statistics_pb2.BytesStatistics]:
      """Retrieve byte statistics if available."""
      if self._statistics.WhichOneof("stats") == "bytes_stats":
          return self._statistics.bytes_stats
      return None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  from typing import Dict, Iterable, Optional, Sequence, Text, Tuple, Union
  import numpy
  import pyarrow
  import tensorflow
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.arrow import arrow_util
  from tensorflow_data_validation.utils import artifacts_io_impl
  from tensorflow_data_validation.utils import io_util
  from tfx_bsl import statistics
  from tfx_bsl.arrow import array_util
  from google.protobuf import text_format
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 6 for calling the FeatureView obj's underlying member method code with example usage: obj.bytes_statistics() and returns: None, self