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 __init__(
      self,
      keys: List[metric_types.MetricKey],
      model_name: str,
      model_loader: types.ModelLoader,
      eval_config: Optional[config_pb2.EvalConfig],
      desired_batch_size: Optional[int] = None,
  ):
      super().__init__(
          keys,
          model_name,
          model_loader,
          eval_config,
          desired_batch_size,
          "keras_compiled_metrics_combine",
      )
  
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 1 to create a new _KerasCompiledMetricsCombiner object from inherited base classes: _KerasCombiner with example: obj = _KerasCompiledMetricsCombiner(keys, model_name, model_loader, eval_config, desired_batch_size)