code: def stringify_slice_key(slice_key: SliceKeyType) -> str:
      """Stringifies a slice key.
  
      The string representation of a SingletonSliceKeyType is "feature:value". When
      multiple columns / features are specified, the string representation of a
      SliceKeyType is "c1_X_c2_X_...:v1_X_v2_X_..." where c1, c2, ... are the column
      names and v1, v2, ... are the corresponding values For example,
      ('gender, 'f'), ('age', 5) befores age_X_gender:f_X_5. If no columns / feature
      specified, return "Overall".
  
      Note that we do not perform special escaping for slice values that contain
      '_X_'. This stringified representation is meant to be human-readbale rather
      than a reversible encoding.
  
      The columns will be in the same order as in SliceKeyType. If they are
      generated using SingleSliceSpec.generate_slices, they will be in sorted order,
      ascending.
  
      Technically float values are not supported, but we don't check for them here.
  
      Args:
        slice_key: Slice key to stringify. The constituent SingletonSliceKeyTypes
          should be sorted in ascending order.
  
      Returns:
        String representation of the slice key.
      """
      key_count = len(slice_key)
      if not key_count:
          return OVERALL_SLICE_NAME
  
      keys = []
      values = []
      separator = "_X_"
  
      for feature, value in slice_key:
          # Since this is meant to be a human-readable string, we assume that the
          # feature and feature value are valid UTF-8 strings (might not be true in
          # cases where people store serialised protos in the features for instance).
          keys.append(tf.compat.as_text(feature))
          # We need to call as_str_any to convert non-string (e.g. integer) values to
          # string first before converting to text.
          values.append(tf.compat.as_text(tf.compat.as_str_any(value)))
  
      # To use u'{}' instead of '{}' here to avoid encoding a unicode character with
      # ascii codec.
      return (
          separator.join(["{}".format(key) for key in keys])
          + ":"
          + separator.join(["{}".format(value) for value in 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 8 for calling a global function with example usage: stringify_slice_key(slice_key) and returns: unknown, OVERALL_SLICE_NAME