class_docstr: Specification for a cross slice.
  
  This is intended to be an immutable class that specifies a cross slice.
  Use this in conjunction with get_slices_for_features_dicts to generate slices
  for dictionaries of features.
  
  Attributes:
    base_slicing_spec: The baseline slicing spec.
    slicing_specs: A tuple of slicing specs of the proto counterparts.
class_docstr_tok: ["Specification","for","a","cross","slice",".","This","is","intended","to","be","an","immutable","class","that","specifies","a","cross","slice",".","Use","this","in","conjunction","with","get_slices_for_features_dicts","to","generate","slices","for","dictionaries","of","features",".","Attributes",":","base_slicing_spec",":","The","baseline","slicing","spec",".","slicing_specs",":","A","tuple","of","slicing","specs","of","the","proto","counterparts","."]
docstr: Create a new CrrossSliceSpec object from its Proto counnterpart.
code: def __new__(cls, spec: config_pb2.CrossSlicingSpec):
      """Create a new CrrossSliceSpec object from its Proto counnterpart."""
      # This is organized as a Tuple(baseline_spec, Tuple(slice_specs))
      return super(CrossSliceSpec, cls).__new__(
          cls,
          SingleSliceSpec(spec=spec.baseline_spec),
          tuple(
              SingleSliceSpec(spec=slicing_spec)
              for slicing_spec in spec.slicing_specs
          ),
      )
  
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 1 for calling the CrossSliceSpec obj's underlying member method code with example usage: obj.__new__(cls, spec) and returns: super