line indents

api.bs

@@ -1105,10 +1105,10 @@ parameters:
 3. the `filterData` value used for selecting relevant events;
 4. the `PrivateAttributionLogic` such as last-touch or equal-credit;
 5. two sensitivity parameters: `report_global_sensitivity` which is a cap on how much attributed
-  value can come from this one conversion (e.g. the conversion value) and `query_global_sensitivity`
-  which is a maximum sensitivity across all reports to be processed the aggregation query.
+    value can come from this one conversion (e.g. the conversion value) and `query_global_sensitivity`
+    which is a maximum sensitivity across all reports to be processed the aggregation query.
 6. the p-norm to use when bounding the histogram contribution's sensitivity. 1-norm corresponding
-  to using Laplace noise in aggregation query and 2-norm for Gaussian noise.
+    to using Laplace noise in aggregation query and 2-norm for Gaussian noise.
 
 The algorithm to <dfn>deduct privacy budget</dfn> and compute the attributed histogram will first look across
 epochs for eligible impressions. It will deduct budget from any epoch with eligible
@@ -1119,10 +1119,10 @@ Step 1: select relevant impressions within an epoch using the `filterData`.
 Step 2: For each epoch compute the individual privacy loss of the query following Thm 4 of [[PPA-DP]]. There are three cases
 * Case 1: If the epoch has no relevant impressions the privacy loss is 0.
 * Case 2: If the window of epochs contains only a single epoch, the `individual_sensitivity` is the p-norm of attribution function
-  applied to only the impressions in this epoch. The privacy loss deducted from the epoch's budget is
-  then `requested_epsilon * individual_sensitivity / query_global_sensitivity`.
+    applied to only the impressions in this epoch. The privacy loss deducted from the epoch's budget is
+    then `requested_epsilon * individual_sensitivity / query_global_sensitivity`.
 * Case 3: If multiple epochs are considered, the privacy loss deducted from the epoch's budget is
-  `requested_epsilon * report_global_sensitivity / query_global_sensitivity`
+    `requested_epsilon * report_global_sensitivity / query_global_sensitivity`
 
 Step 3: Attempt to deduct the privacy of the epoch, if the filter has sufficient budget the impressions
 are added to the set to be considered for attribution; otherise, they are dropped.