class_docstr: Computes the precision of the predictions with respect to the labels.
  
  The metric uses true positives and false positives to compute precision by
  dividing the true positives by the sum of true positives and false positives.
  
  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_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 ObjectDetectionPrecision obj's underlying member method code with example usage: obj._default_name() and returns: OBJECT_DETECTION_PRECISION_NAME