def METHOD_NAME(  # pylint: disable=invalid-name
    model_fn, thresholds=None, model_dir=None, config=None,
    feature_engineering_fn=None):
  """Builds a logistic regression Estimator for binary classification.
  THIS CLASS IS DEPRECATED. See
  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
  for general migration instructions.
  This method provides a basic Estimator with some additional metrics for custom
  binary classification models, including AUC, precision/recall and accuracy.
  Example:
  ```python
    # See tf.contrib.learn.Estimator(...) for details on model_fn structure
    def my_model_fn(...):
      pass
    estimator = LogisticRegressor(model_fn=my_model_fn)
    # Input builders
    def input_fn_train:
      pass
    estimator.fit(input_fn=input_fn_train)
    estimator.predict(x=x)
  ```
  Args:
    model_fn: Model function with the signature:
      `(features, labels, mode) -> (predictions, loss, train_op)`.
      Expects the returned predictions to be probabilities in [0.0, 1.0].
    thresholds: List of floating point thresholds to use for accuracy,
      precision, and recall metrics. If `None`, defaults to `[0.5]`.
    model_dir: Directory to save model parameters, graphs, etc. This can also
      be used to load checkpoints from the directory into a estimator to
      continue training a previously saved model.
    config: A RunConfig configuration object.
    feature_engineering_fn: Feature engineering function. Takes features and
                      labels which are the output of `input_fn` and
                      returns features and labels which will be fed
                      into the model.
  Returns:
    An `Estimator` instance.
  """
  return estimator.Estimator(
      model_fn=_get_model_fn_with_logistic_metrics(model_fn),
      model_dir=model_dir,
      config=config,
      params={'thresholds': thresholds},
      feature_engineering_fn=feature_engineering_fn)