class_docstr: A combiner which computes boolean flip rate metrics.
class_docstr_tok: ["A","combiner","which","computes","boolean","flip","rate","metrics","."]
code: def __init__(
      self,
      threshold: float,
      eval_config: config_pb2.EvalConfig,
      baseline_model_name: str,
      model_name: str,
      output_name: str,
      flip_rate_key: metric_types.MetricKey,
      neg_to_neg_key: metric_types.MetricKey,
      neg_to_pos_key: metric_types.MetricKey,
      pos_to_neg_key: metric_types.MetricKey,
      pos_to_pos_key: metric_types.MetricKey,
      example_weighted: bool,
  ):
      self._threshold = threshold
      self._eval_config = eval_config
      self._baseline_model_name = baseline_model_name
      self._model_name = model_name
      self._output_name = output_name
      self._flip_rate_key = flip_rate_key
      self._neg_to_neg_key = neg_to_neg_key
      self._neg_to_pos_key = neg_to_pos_key
      self._pos_to_neg_key = pos_to_neg_key
      self._pos_to_pos_key = pos_to_pos_key
      self._example_weighted = example_weighted
  
file_path: model-analysis/tensorflow_model_analysis/metrics/flip_metrics.py
imports: import dataclasses
  from typing import Optional, Iterable, List
  import apache_beam
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.utils import model_util
label_desc: Use image node_id 1 to create a new _BooleanFlipRatesCombiner object from inherited base classes: beam with example: obj = _BooleanFlipRatesCombiner(threshold, eval_config, baseline_model_name, model_name, output_name, flip_rate_key, neg_to_neg_key, neg_to_pos_key, pos_to_neg_key, pos_to_pos_key, example_weighted)