class_docstr: Combines compilable metric weights and computes result.
class_docstr_tok: ["Combines","compilable","metric","weights","and","computes","result","."]
code: def setup(self):
      if self._metrics is None:
          self._metrics = {}
          with tf.keras.utils.custom_object_scope(
              _load_custom_objects(self._custom_objects)
          ):
              for i, output_name in enumerate(self._output_names):
                  self._metrics[output_name] = _deserialize_metrics(
                      self._metric_configs[i]
                  )
                  for loss in _deserialize_losses(
                      self._loss_configs[i]
                  ):
                      self._metrics[output_name].append(
                          _LossMetric(loss)
                      )
  
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 2 for calling the _CompilableMetricsCombiner obj's underlying member method code with example usage: obj.setup() without return types