tensorflow_model_analysis/metrics/calibration.py [221:250]:
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    eval_config: Optional[config_pb2.EvalConfig] = None,
    model_name: str = '',
    output_name: str = '',
    sub_key: Optional[metric_types.SubKey] = None,
    aggregation_type: Optional[metric_types.AggregationType] = None,
    class_weights: Optional[Dict[int, float]] = None,
    example_weighted: bool = False) -> metric_types.MetricComputations:
  """Returns metric computations for weighted labels, predictions, and examples.

  Args:
    name: Metric name.
    eval_config: Eval config.
    model_name: Optional model name (if multi-model evaluation).
    output_name: Optional output name (if multi-output model type).
    sub_key: Optional sub key.
    aggregation_type: Optional aggregation type.
    class_weights: Optional class weights to apply to multi-class / multi-label
      labels and predictions prior to flattening (when micro averaging is used).
    example_weighted: True if example weights should be applied.
  """
  key = metric_types.MetricKey(
      name=name,
      model_name=model_name,
      output_name=output_name,
      sub_key=sub_key,
      example_weighted=example_weighted)
  return [
      metric_types.MetricComputation(
          keys=[key],
          preprocessor=None,  # Use default
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tensorflow_model_analysis/metrics/squared_pearson_correlation.py [45:62]:
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    eval_config: Optional[config_pb2.EvalConfig] = None,
    model_name: str = '',
    output_name: str = '',
    sub_key: Optional[metric_types.SubKey] = None,
    aggregation_type: Optional[metric_types.AggregationType] = None,
    class_weights: Optional[Dict[int, float]] = None,
    example_weighted: bool = False) -> metric_types.MetricComputations:
  """Returns metric computations for squared pearson correlation (r^2)."""
  key = metric_types.MetricKey(
      name=name,
      model_name=model_name,
      output_name=output_name,
      sub_key=sub_key,
      example_weighted=example_weighted)
  return [
      metric_types.MetricComputation(
          keys=[key],
          preprocessor=None,
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