class_docstr: Default anomaly detection model that balances efficiency with performance.
code: def _default_post_rule_train_config(self):
      from merlion.evaluate.anomaly import TSADMetric
  
      return dict(metric=TSADMetric.F1, unsup_quantile=None)
  
imports: import logging
  from typing import Optional, Tuple
  from merlion.models.factory import ModelFactory
  from merlion.models.layers import LayeredDetector, LayeredForecaster, LayeredModelConfig
  from merlion.models.anomaly.base import DetectorBase
  from merlion.models.forecast.base import ForecasterBase
  from merlion.utils import TimeSeries
label_desc: Use image node_id 1 for calling the DefaultDetector obj's underlying member method code with example usage: obj._default_post_rule_train_config() and returns: dict