resolved conflicts

bayesflow/adapters/transforms/as_set.py

@@ -6,9 +6,16 @@
 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:
 

bayesflow/adapters/transforms/concatenate.py

@@ -12,6 +12,7 @@
 
 @serializable(package="bayesflow.adapters")
 class Concatenate(Transform):
+<<<<<<< HEAD
     """Concatenate multiple arrays into a new key. Used to specify how data variables should be treated by the network.
 
     Parameters:
@@ -23,6 +24,14 @@ class Concatenate(Transform):
     Example:
     Suppose you have a simulator that generates variables beta, sigma from priors  and then observation 
     variables "x" and "y". We can then use concatonate in the following way
+=======
+    """Concatenate multiple arrays into a new key.
+    Parameters:
+
+    keys:
+
+    into:
+>>>>>>> b8b68757b0ae1a5f34bf656a837abbeb77e2ec62
 
     adapter = (
         bf.Adapter()

bayesflow/adapters/transforms/constrain.py

@@ -32,17 +32,20 @@ class Constrain(ElementwiseTransform):
 
 
     Examples:
-        Let sigma be the standard deviation of a normal distribution, then sigma should always be greater than zero.
+        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
+        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

@@ -25,8 +25,12 @@ 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"))
             )