def default_evaluators(eval_shared_model: Optional[Union[(types.EvalSharedModel, Dict[(Text, types.EvalSharedModel)])]]=None, eval_config: config.EvalConfig=None, compute_confidence_intervals: Optional[bool]=False, k_anonymization_count: int=1, desired_batch_size: Optional[int]=None, serialize: bool=False, random_seed_for_testing: Optional[int]=None) -> List[evaluator.Evaluator]:
    'Returns the default evaluators for use in ExtractAndEvaluate.\n\n  Args:\n    eval_shared_model: Optional shared model (single-model evaluation) or dict\n      of shared models keyed by model name (multi-model evaluation). Only\n      required if there are metrics to be computed in-graph using the model.\n    eval_config: Eval config.\n    compute_confidence_intervals: Deprecated (use eval_config).\n    k_anonymization_count: Deprecated (use eval_config).\n    desired_batch_size: Optional batch size for batching in combiner.\n    serialize: Deprecated.\n    random_seed_for_testing: Provide for deterministic tests only.\n  '
    disabled_outputs = []
    if eval_config:
        eval_config = config.update_eval_config_with_defaults(eval_config)
        disabled_outputs = eval_config.options.disabled_outputs.values
        if (model_util.get_model_types(eval_config) == set([constants.TF_LITE])):
            if eval_shared_model:
                if isinstance(eval_shared_model, dict):
                    eval_shared_model = {k: v._replace(include_default_metrics=False) for (k, v) in eval_shared_model.items()}
                else:
                    eval_shared_model = eval_shared_model._replace(include_default_metrics=False)
    if ((constants.METRICS_KEY in disabled_outputs) and (constants.PLOTS_KEY in disabled_outputs)):
        return []
    if _is_legacy_eval(eval_shared_model, eval_config):
        if (eval_config is not None):
            if eval_config.options.HasField('compute_confidence_intervals'):
                compute_confidence_intervals = eval_config.options.compute_confidence_intervals.value
            if eval_config.options.HasField('k_anonymization_count'):
                k_anonymization_count = eval_config.options.k_anonymization_count.value
        return [metrics_and_plots_evaluator.MetricsAndPlotsEvaluator(eval_shared_model, compute_confidence_intervals=compute_confidence_intervals, k_anonymization_count=k_anonymization_count, desired_batch_size=desired_batch_size, serialize=serialize, random_seed_for_testing=random_seed_for_testing)]
    else:
        return [metrics_and_plots_evaluator_v2.MetricsAndPlotsEvaluator(eval_config=eval_config, eval_shared_model=eval_shared_model)]
Returns the default evaluators for use in ExtractAndEvaluate.

Args:
  eval_shared_model: Optional shared model (single-model evaluation) or dict
    of shared models keyed by model name (multi-model evaluation). Only
    required if there are metrics to be computed in-graph using the model.
  eval_config: Eval config.
  compute_confidence_intervals: Deprecated (use eval_config).
  k_anonymization_count: Deprecated (use eval_config).
  desired_batch_size: Optional batch size for batching in combiner.
  serialize: Deprecated.
  random_seed_for_testing: Provide for deterministic tests only.
def default_evaluators(eval_shared_model: Optional[Union[(types.EvalSharedModel, Dict[(Text, types.EvalSharedModel)])]]=None, eval_config: config.EvalConfig=None, compute_confidence_intervals: Optional[bool]=False, k_anonymization_count: int=1, desired_batch_size: Optional[int]=None, serialize: bool=False, random_seed_for_testing: Optional[int]=None) -> List[evaluator.Evaluator]:
    'Returns the default evaluators for use in ExtractAndEvaluate.\n\n  Args:\n    eval_shared_model: Optional shared model (single-model evaluation) or dict\n      of shared models keyed by model name (multi-model evaluation). Only\n      required if there are metrics to be computed in-graph using the model.\n    eval_config: Eval config.\n    compute_confidence_intervals: Deprecated (use eval_config).\n    k_anonymization_count: Deprecated (use eval_config).\n    desired_batch_size: Optional batch size for batching in combiner.\n    serialize: Deprecated.\n    random_seed_for_testing: Provide for deterministic tests only.\n  '
    disabled_outputs = []
    if eval_config:
        eval_config = config.update_eval_config_with_defaults(eval_config)
        disabled_outputs = eval_config.options.disabled_outputs.values
        if (model_util.get_model_types(eval_config) == set([constants.TF_LITE])):
            if eval_shared_model:
                if isinstance(eval_shared_model, dict):
                    eval_shared_model = {k: v._replace(include_default_metrics=False) for (k, v) in eval_shared_model.items()}
                else:
                    eval_shared_model = eval_shared_model._replace(include_default_metrics=False)
    if ((constants.METRICS_KEY in disabled_outputs) and (constants.PLOTS_KEY in disabled_outputs)):
        return []
    if _is_legacy_eval(eval_shared_model, eval_config):
        if (eval_config is not None):
            if eval_config.options.HasField('compute_confidence_intervals'):
                compute_confidence_intervals = eval_config.options.compute_confidence_intervals.value
            if eval_config.options.HasField('k_anonymization_count'):
                k_anonymization_count = eval_config.options.k_anonymization_count.value
        return [metrics_and_plots_evaluator.MetricsAndPlotsEvaluator(eval_shared_model, compute_confidence_intervals=compute_confidence_intervals, k_anonymization_count=k_anonymization_count, desired_batch_size=desired_batch_size, serialize=serialize, random_seed_for_testing=random_seed_for_testing)]
    else:
        return [metrics_and_plots_evaluator_v2.MetricsAndPlotsEvaluator(eval_config=eval_config, eval_shared_model=eval_shared_model)]