class_docstr: Aggregates metrics using keras compiled_metrics and compiled_loss.
class_docstr_tok: ["Aggregates","metrics","using","keras","compiled_metrics","and","compiled_loss","."]
code: def _create_accumulator(
      self,
  ) -> tf_metric_accumulators.TFCompilableMetricsAccumulator:
      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,
          self._output_counts,
          desired_batch_size=self._desired_batch_size,
      )
  
imports: import collections
  from itertools import chain
  from typing import Dict, Iterable, List, Optional
  import apache_beam
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  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
  from tensorflow_model_analysis.utils import util
label_desc: Use image node_id 3 for calling the _KerasCompiledMetricsCombiner obj's underlying member method code with example usage: obj._create_accumulator() and returns: tf_metric_accumulators