class_docstr: A combiner which computes cross entropy metrics.
  
  Two importnat parameters for cross entropy calcualtion.
    from_logits: (Optional) Whether output is expected to be a logits tensor.
      By default, we consider that output encodes a probability distribution.
    label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For
      example, if `0.1`, use `0.1 / num_classes` for non-target labels and
      `0.9 + 0.1 / num_classes` for target labels.
code: def create_accumulator(self) -> _CrossEntropyAccumulator:
      return _CrossEntropyAccumulator()
  
imports: import abc
  import dataclasses
  from typing import Iterable, Optional, Dict
  import apache_beam
  import numpy
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 3 for calling the _CrossEntropyCombiner obj's underlying member method code with example usage: obj.create_accumulator() and returns: _CrossEntropyAccumulator