class_docstr: Computes the max recall of the predictions with respect to the labels.
  
  The metric uses true positives and false negatives to compute recall by
  dividing the true positives by the sum of true positives and false negatives.
  
  Effectively the recall at threshold = epsilon(1.0e-12). It is equilvalent
  to the recall defined in COCO metrics.
  
  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_MAX_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 ObjectDetectionMaxRecall obj's underlying member method code with example usage: obj._default_name() and returns: OBJECT_DETECTION_MAX_RECALL_NAME