Remove `warn__subtensor_merge_bug` flag

aesara/configdefaults.py

@@ -1454,15 +1454,6 @@ def add_deprecated_configvars():
         in_c_key=False,
     )
 
-    config.add(
-        "warn__subtensor_merge_bug",
-        "Warn if previous versions of Aesara (before 0.5rc2) could have given "
-        "incorrect results when indexing into a subtensor with negative "
-        "stride (for instance, for instance, x[a:b:-1][c]).",
-        BoolParam(_warn_default("0.5")),
-        in_c_key=False,
-    )
-
     config.add(
         "warn__gpu_set_subtensor1",
         "Warn if previous versions of Aesara (before 0.6) could have given "

aesara/tensor/basic_opt.py

@@ -2910,13 +2910,6 @@ def merge_two_slices(fgraph, slice1, len1, slice2, len2):
             # the k-th element from sl.start but the k-th element from
             # sl.stop backwards
             n_val = sl1.stop - 1 - sl2 * sl1.step
-            if config.warn__subtensor_merge_bug:
-                warnings.warning(
-                    "Your current code is fine, but Aesara versions "
-                    "prior to 0.5rc2 might have given an incorrect result. "
-                    "To disable this warning, set the Aesara flag "
-                    "warn__subtensor_merge_bug to False."
-                )
             # we need to pick either n_val or p_val and then follow same
             # steps as above for covering the index error cases
             val = switch(lt(reverse1, 0), n_val, p_val)

doc/tutorial/loop.rst

@@ -362,7 +362,6 @@ Note that if you want to use a random variable ``d`` that will not be updated th
 
   import aesara
   import aesara.tensor as aet
-  aesara.config.warn__subtensor_merge_bug = False
 
   k = aet.iscalar("k")
   A = aet.vector("A")
@@ -396,7 +395,6 @@ Note that if you want to use a random variable ``d`` that will not be updated th
   import numpy
   import aesara
   import aesara.tensor as aet
-  aesara.config.warn__subtensor_merge_bug = False
 
   coefficients = aesara.tensor.vector("coefficients")
   x = aet.scalar("x")

doc/tutorial/loop_solution_1.py

@@ -10,8 +10,6 @@ import aesara.tensor as aet
 
 # 1. First example
 
-aesara.config.warn__subtensor_merge_bug = False
-
 k = aet.iscalar("k")
 A = aet.vector("A")
 
@@ -58,8 +56,6 @@ print(calculate_polynomial1(test_coeff, 3))
 
 # 3. Reduction performed inside scan
 
-aesara.config.warn__subtensor_merge_bug = False
-
 coefficients = aet.vector("coefficients")
 x = aet.scalar("x")
 max_coefficients_supported = 10000