class_docstr: Combines compilable metric weights and computes result.
class_docstr_tok: ["Combines","compilable","metric","weights","and","computes","result","."]
code: def create_accumulator(
      self,
  ) -> tf_metric_accumulators.TFCompilableMetricsAccumulator:
      configs = zip(self._metric_configs, self._loss_configs)
      padding_options = None
      if self._eval_config is not None:
          model_spec = model_util.get_model_spec(
              self._eval_config, self._model_name
          )
          if model_spec is not None and model_spec.HasField(
              "padding_options"
          ):
              padding_options = model_spec.padding_options
  
      return tf_metric_accumulators.TFCompilableMetricsAccumulator(
          padding_options,
          [len(m) + len(l) for m, l in configs],
          desired_batch_size=self._desired_batch_size,
      )
  
imports: import collections
  import importlib
  import itertools
  from typing import Any, Dict, Iterable, List, Optional, Type, Tuple, Union
  import apache_beam
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.metrics import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import tf_metric_accumulators
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.utils import model_util
label_desc: Use image node_id 4 for calling the _CompilableMetricsCombiner obj's underlying member method code with example usage: obj.create_accumulator() and returns: tf_metric_accumulators