class_docstr: Computes best precision where recall is >= specified value.
  
  The threshold for the given recall value is computed and used to evaluate the
  corresponding precision.
  
  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.
code: def _default_name(self) -> str:
      return OBJECT_DETECTION_PRECISION_AT_RECALL_NAME
  
imports: from typing import List, Optional, Tuple, Union
  from tensorflow_model_analysis.metrics import confusion_matrix_metrics
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import preprocessors
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 2 for calling the ObjectDetectionPrecisionAtRecall obj's underlying member method code with example usage: obj._default_name() and returns: OBJECT_DETECTION_PRECISION_AT_RECALL_NAME