class_docstr: Aggregates metrics using keras model.evaluate method.
class_docstr_tok: ["Aggregates","metrics","using","keras","model.evaluate","method","."]
code: def _create_accumulator(
      self,
  ) -> tf_metric_accumulators.TFMetricsAccumulator:
      return tf_metric_accumulators.TFMetricsAccumulator(
          # Separate inputs are tracked for (inputs, labels, example_weights).
          # Since the inputs are the same for each output, only the first output
          # index will set the input data.
          input_counts=[3] * len(self._output_counts),
          metric_counts=self._output_counts,
          size_estimator_fn=len,
          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 _KerasEvaluateCombiner obj's underlying member method code with example usage: obj._create_accumulator() and returns: tf_metric_accumulators