hopefully everything resolved

Author: eodole

bayesflow/adapters/transforms/as_set.py

@@ -6,17 +6,11 @@
 class AsSet(ElementwiseTransform):
     """
         The `.as_set(["x", "y"])` transform indicates that both `x` and `y` are treated as sets.
-    <<<<<<< HEAD
-        That is, their values will be treated as *exchangable* such that they will imply the same inference regardless of
-        the values' order. This would be useful in a linear regression context where we can index the observations in
-        arbitrary order and always get the same regression line.
-    =======
         That is, their values will be treated as *exchangable* such that they will imply
         the same inference regardless of the values' order.
         This is useful, for example, in a linear regression context where we can index
         the observations in arbitrary order and always get the same regression line.
-    >>>>>>> b8b68757b0ae1a5f34bf656a837abbeb77e2ec62
-
+        
         Useage:
 
         adapter = (

bayesflow/adapters/transforms/constrain.py

@@ -32,20 +32,18 @@ class Constrain(ElementwiseTransform):
 
 
     Examples:
-        Let sigma be the standard deviation of a normal distribution,
+        1) Let sigma be the standard deviation of a normal distribution,
         then sigma should always be greater than zero.
 
-        Useage:
         Useage:
         adapter = (
             bf.Adapter()
             .constrain("sigma", lower=0)
             )
 
-        Suppose p is the parameter for a binomial distribution where p must be in [0,1]
-        then we would constrain the neural network to estimate p in the following way.
+        2 ) Suppose p is the parameter for a binomial distribution where p must be in 
+        [0,1] then we would constrain the neural network to estimate p in the following way.
 
-        Usage:
         Usage:
         adapter = (
             bf.Adapter()

bayesflow/adapters/transforms/keep.py

@@ -25,13 +25,9 @@ class Keep(Transform):
 
             adapter = (
                 bf.adapters.Adapter()
-    <<<<<<< HEAD
-
-                # drop data from unneeded priors alpha, and r
-    =======
-                # only keep theta and x
-    >>>>>>> b8b68757b0ae1a5f34bf656a837abbeb77e2ec62
-                .keep(("theta", "x"))
+                    # drop data from unneeded priors alpha, and r
+                    # only keep theta and x
+                    .keep(("theta", "x"))
                 )
 
         Example: