class_docstr: Specification for a single slice.
  
  This is intended to be an immutable class that specifies a single slice.
  Use this in conjunction with get_slices_for_features_dicts to generate slices
  for dictionaries of features.
  
  Examples:
    - columns = ['age'], features = []
      This means to slice by the 'age' column.
    - columns = ['age'], features = [('gender', 'female')]
      This means to slice by the 'age' column if the 'gender' is 'female'.
    - For more examples, refer to the tests in slicer_test.py.
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code: def to_proto(self) -> config_pb2.SlicingSpec:
      feature_values = {k: str(v) for (k, v) in self._features}
      return config_pb2.SlicingSpec(
          feature_keys=self._columns, feature_values=feature_values
      )
  
imports: import itertools
  from typing import Any, Callable, Dict, Generator, Iterable, List, NamedTuple, Optional, Tuple, Union
  import apache_beam
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
  from tensorflow_model_analysis.slicer import slice_accessor
label_desc: Use image node_id 6 for calling the SingleSliceSpec obj's underlying member method code with example usage: obj.to_proto() and returns: config_pb2