(condq, cutoff=0.95, min_batches=2)
return intersection_from_conditional_quantile(condq,
            statistic=intersection_over_union,
            cutoff=cutoff, min_batches=min_batches)
Given a RunningConditionalQuantile, estimates all-pairs
    IoU statistics for all units and conditions at the specified
    quantile cutoff.  Note that cutoff can be a list of cutoffs.
    The result is a tensor of dimension (units, conditions, cutoffs)
    containing IoU estimates for each combination.

    Conditions that are sampled in fewer than min_batches are given IoU 0.
Given a RunningConditionalQuantile, estimates all-pairs
    IoU statistics for all units and conditions at the specified
    quantile cutoff.  Note that cutoff can be a list of cutoffs.
    The result is a tensor of dimension (units, conditions, cutoffs)
    containing IoU estimates for each combination.
Given a RunningConditionalQuantile estimates all - pairs IoU statistics for all units and conditions at the specified quantile cutoff . Note that cutoff can be a list of cutoffs . The result is a tensor of dimension ( units conditions cutoffs ) containing IoU estimates for each combination .
def iou_from_conditional_quantile(condq, cutoff=0.95, min_batches=2):
    '''
    Given a RunningConditionalQuantile, estimates all-pairs
    IoU statistics for all units and conditions at the specified
    quantile cutoff.  Note that cutoff can be a list of cutoffs.
    The result is a tensor of dimension (units, conditions, cutoffs)
    containing IoU estimates for each combination.

    Conditions that are sampled in fewer than min_batches are given IoU 0.
    '''
    return intersection_from_conditional_quantile(condq,
            statistic=intersection_over_union,
            cutoff=cutoff, min_batches=min_batches)
def iou_from_conditional_quantile ( condq , cutoff = 0.95 , min_batches = 2 ) : return intersection_from_conditional_quantile ( condq , statistic = intersection_over_union , cutoff = cutoff , min_batches = min_batches )