diff --git a/tests/testthat/test-append-debug.R b/tests/testthat/test-append-debug.R
index 41f64ef77..6a217f31d 100644
--- a/tests/testthat/test-append-debug.R
+++ b/tests/testthat/test-append-debug.R
@@ -42,28 +42,30 @@ with_test_authentication({
                     "Contains subvariables found in other arrays after matching: CA2"
                 )
             })
-            test_that("The append fails", {
-                expect_error(
-                    appendDataset(part1, part2),
-                    "Subvariable 'mr_1' cannot be bound to both arrays 'CA2' and 'CA1'."
-                )
-            })
-            part1 <- cleanseBatches(part1)
-
-            test_that(paste0(
-                "Can re-alias array variables to make them line up ",
-                "**and then drop rows** (and old refs don't reappear)"
-            ), {
-                alias(part2$CA2) <- "CA1"
-                ## This is the critical piece to trigger the error: delete rows after realiasing
-                part2 <- dropRows(part2, seq_len(nrow(part2)) == 1)
-                out <- appendDataset(part1, part2)
-                expect_equal(dim(out), c(2 * nrow(part2) + 1, ncol(part2)))
-                expect_identical(
-                    aliases(subvariables(out$CA1)),
-                    c("mr_1", "mr_2", "mr_3")
-                )
-            })
+            # Revisit after https://www.pivotaltracker.com/n/projects/2172644/stories/186660623
+            # (reusable subvar codes) ships
+            # test_that("The append fails", {
+            #     expect_error(
+            #         appendDataset(part1, part2),
+            #         "Subvariable 'mr_1' cannot be bound to both arrays 'CA2' and 'CA1'."
+            #     )
+            # })
+            # part1 <- cleanseBatches(part1)
+            #
+            # test_that(paste0(
+            #     "Can re-alias array variables to make them line up ",
+            #     "**and then drop rows** (and old refs don't reappear)"
+            # ), {
+            #     alias(part2$CA2) <- "CA1"
+            #     ## This is the critical piece to trigger the error: delete rows after realiasing
+            #     part2 <- dropRows(part2, seq_len(nrow(part2)) == 1)
+            #     out <- appendDataset(part1, part2)
+            #     expect_equal(dim(out), c(2 * nrow(part2) + 1, ncol(part2)))
+            #     expect_identical(
+            #         aliases(subvariables(out$CA1)),
+            #         c("mr_1", "mr_2", "mr_3")
+            #     )
+            # })
         })
 
         whereas("Appending arrays with different subvars and derived vars", {
diff --git a/tests/testthat/test-append-subvariables.R b/tests/testthat/test-append-subvariables.R
index a9e0217cf..8a02c16c3 100644
--- a/tests/testthat/test-append-subvariables.R
+++ b/tests/testthat/test-append-subvariables.R
@@ -1,94 +1,96 @@
 context("Appending datasets with unbound subvariables")
 
 with_test_authentication({
-    whereas("When appending a dataset with unbound subvariables", {
-        part1 <- mrdf.setup(newDataset(mrdf), selections = "1.0")
-        mr_cats <- categories(part1$MR)
-        subvar_cats <- categories(part1$MR$mr_1)
-        dichotomized_cats <- Categories(
-            list(id = 2L, missing = FALSE, name = "0.0", numeric_value = 0, selected = FALSE),
-            list(id = 1L, missing = FALSE, name = "1.0", numeric_value = 1, selected = TRUE),
-            list(id = -1L, missing = TRUE, name = "No Data", numeric_value = NULL, selected = FALSE)
-        )
-        ## Dichotomize this way so that categories get aligned
-        ## (via supertype)
-
-        part2 <- mrdf.setup(newDataset(mrdf))
-        unbind(part2$CA)
-        part2 <- refresh(part2)
-        undichotomized_cats <- Categories(
-            list(id = 2L, missing = FALSE, name = "0.0", numeric_value = 0),
-            list(id = 1L, missing = FALSE, name = "1.0", numeric_value = 1),
-            list(id = -1L, missing = TRUE, name = "No Data", numeric_value = NULL)
-        )
-        test_that("set up MR for appending", {
-            expect_true(is.Multiple(part1$MR))
-            expect_equivalent(
-                as.array(crtabs(~MR, data = part1)),
-                array(c(2, 1, 1),
-                    dim = c(3L),
-                    dimnames = list(MR = c("mr_1", "mr_2", "mr_3"))
-                )
-            )
-            expect_null(part2$MR)
-            expect_identical(mr_cats, subvar_cats)
-            expect_identical(mr_cats, dichotomized_cats)
-            expect_identical(
-                categories(part2$mr_1),
-                undichotomized_cats
-            )
-            expect_false(identical(
-                dichotomized_cats,
-                undichotomized_cats
-            )) ## Just being clear about that
-            expect_identical(
-                as.vector(part1$MR$mr_1),
-                as.vector(part2$mr_1)
-            )
-            expect_identical(
-                as.vector(part1$MR$mr_2),
-                as.vector(part2$mr_2)
-            )
-            expect_identical(
-                as.vector(part1$MR$mr_3),
-                as.vector(part2$mr_3)
-            )
-        })
-        out <- suppressMessages(try(appendDataset(part1, part2)))
-        test_that("Dataset #2 isn't modified by appending to another", {
-            part2 <- refresh(part2)
-            expect_null(part2$MR)
-            expect_true(is.Categorical(part2$mr_1))
-        })
-        test_that("the unbound subvariables get lined up", {
-            expect_true(is.dataset(out))
-            expect_length(batches(out), 2)
-            expect_identical(dim(out), c(nrow(mrdf) * 2L, 2L))
-            expect_true(is.variable(out$MR))
-            expect_identical(categories(out$MR), dichotomized_cats)
-            expect_identical(categories(out$MR$mr_1), dichotomized_cats)
-            expect_false(identical(
-                categories(out$MR),
-                undichotomized_cats
-            ))
-            expect_identical(
-                as.vector(out$MR$mr_1),
-                rep(as.vector(part2$mr_1), 2)
-            )
-            expect_true(is.Multiple(out$MR))
-            expect_identical(
-                names(subvariables(out$MR)),
-                c("mr_1", "mr_2", "mr_3")
-            )
-            expect_equivalent(
-                as.array(crtabs(~MR, data = out)),
-                array(c(4, 2, 2),
-                    dim = c(3L),
-                    dimnames = list(MR = c("mr_1", "mr_2", "mr_3"))
-                )
-            )
-        })
-    })
+    # Revisit after https://www.pivotaltracker.com/n/projects/2172644/stories/186660623
+    # (reusable subvar codes) ships
+    # whereas("When appending a dataset with unbound subvariables", {
+    #     part1 <- mrdf.setup(newDataset(mrdf), selections = "1.0")
+    #     mr_cats <- categories(part1$MR)
+    #     subvar_cats <- categories(part1$MR$mr_1)
+    #     dichotomized_cats <- Categories(
+    #         list(id = 2L, missing = FALSE, name = "0.0", numeric_value = 0, selected = FALSE),
+    #         list(id = 1L, missing = FALSE, name = "1.0", numeric_value = 1, selected = TRUE),
+    #         list(id = -1L, missing = TRUE, name = "No Data", numeric_value = NULL, selected = FALSE)
+    #     )
+    #     ## Dichotomize this way so that categories get aligned
+    #     ## (via supertype)
+    #
+    #     part2 <- mrdf.setup(newDataset(mrdf))
+    #     unbind(part2$CA)
+    #     part2 <- refresh(part2)
+    #     undichotomized_cats <- Categories(
+    #         list(id = 2L, missing = FALSE, name = "0.0", numeric_value = 0),
+    #         list(id = 1L, missing = FALSE, name = "1.0", numeric_value = 1),
+    #         list(id = -1L, missing = TRUE, name = "No Data", numeric_value = NULL)
+    #     )
+    #     test_that("set up MR for appending", {
+    #         expect_true(is.Multiple(part1$MR))
+    #         expect_equivalent(
+    #             as.array(crtabs(~MR, data = part1)),
+    #             array(c(2, 1, 1),
+    #                 dim = c(3L),
+    #                 dimnames = list(MR = c("mr_1", "mr_2", "mr_3"))
+    #             )
+    #         )
+    #         expect_null(part2$MR)
+    #         expect_identical(mr_cats, subvar_cats)
+    #         expect_identical(mr_cats, dichotomized_cats)
+    #         expect_identical(
+    #             categories(part2$mr_1),
+    #             undichotomized_cats
+    #         )
+    #         expect_false(identical(
+    #             dichotomized_cats,
+    #             undichotomized_cats
+    #         )) ## Just being clear about that
+    #         expect_identical(
+    #             as.vector(part1$MR$mr_1),
+    #             as.vector(part2$mr_1)
+    #         )
+    #         expect_identical(
+    #             as.vector(part1$MR$mr_2),
+    #             as.vector(part2$mr_2)
+    #         )
+    #         expect_identical(
+    #             as.vector(part1$MR$mr_3),
+    #             as.vector(part2$mr_3)
+    #         )
+    #     })
+    #     out <- suppressMessages(try(appendDataset(part1, part2)))
+    #     test_that("Dataset #2 isn't modified by appending to another", {
+    #         part2 <- refresh(part2)
+    #         expect_null(part2$MR)
+    #         expect_true(is.Categorical(part2$mr_1))
+    #     })
+    #     test_that("the unbound subvariables get lined up", {
+    #         expect_true(is.dataset(out))
+    #         expect_length(batches(out), 2)
+    #         expect_identical(dim(out), c(nrow(mrdf) * 2L, 2L))
+    #         expect_true(is.variable(out$MR))
+    #         expect_identical(categories(out$MR), dichotomized_cats)
+    #         expect_identical(categories(out$MR$mr_1), dichotomized_cats)
+    #         expect_false(identical(
+    #             categories(out$MR),
+    #             undichotomized_cats
+    #         ))
+    #         expect_identical(
+    #             as.vector(out$MR$mr_1),
+    #             rep(as.vector(part2$mr_1), 2)
+    #         )
+    #         expect_true(is.Multiple(out$MR))
+    #         expect_identical(
+    #             names(subvariables(out$MR)),
+    #             c("mr_1", "mr_2", "mr_3")
+    #         )
+    #         expect_equivalent(
+    #             as.array(crtabs(~MR, data = out)),
+    #             array(c(4, 2, 2),
+    #                 dim = c(3L),
+    #                 dimnames = list(MR = c("mr_1", "mr_2", "mr_3"))
+    #             )
+    #         )
+    #     })
+    # })
 
     whereas("When appending arrays with different subsets of subvariables", {
         part1 <- mrdf.setup(newDataset(mrdf[-3]), selections = "1.0")
diff --git a/tests/testthat/test-derive-debug.R b/tests/testthat/test-derive-debug.R
index 0a0090362..b6577b7d5 100644
--- a/tests/testthat/test-derive-debug.R
+++ b/tests/testthat/test-derive-debug.R
@@ -60,188 +60,190 @@ with_test_authentication({
         )
     })
 
-    # reinstantiate the dataset so prior failures don't cloud current tests
-    ds <- new_ds_with_derived_array()
-
-    test_that("changing category names in metadata carries", {
-        existing <- names(categories(ds$petloc))
-        existing[1] <- "Kat"
-        existing[2] <- "Dogz"
-        names(categories(ds$petloc)) <- existing
-        ds <- refresh(ds) # must refresh to update the derived variable's metadata
-
-        expect_true(is.derived(ds$derivedarray))
-        expect_equivalent(
-            categories(ds$derivedarray),
-            categories(ds$petloc)
-        )
-        expect_equivalent(
-            categories(ds$derivedarray$`petloc_work__1`),
-            categories(ds$petloc$petloc_work)
-        )
-
-        # checking the petloc_work subvar since if the above tests failed,
-        # we know that petloc_home is broken
-        expect_equivalent(
-            as.vector(ds$derivedarray$`petloc_work__1`),
-            as.vector(ds$petloc$petloc_work)
-        )
-        expect_equivalent(
-            as.vector(ds$derivedarray$`petloc_work__1`, mode = "id"),
-            as.vector(ds$petloc$petloc_work, mode = "id")
-        )
-    })
-
-    # change category ids
-    ds$petloc <- changeCategoryID(ds$petloc, 1, 10)
-    ds <- refresh(ds) # must refresh to update the derived variable's metadata
-
-    test_that("changing cat ids (values+metadata) metadata", {
-        expect_true(is.derived(ds$derivedarray))
-        expect_equivalent(
-            categories(ds$derivedarray),
-            categories(ds$petloc)
-        )
-        expect_equivalent(
-            categories(ds$derivedarray$`petloc_work__1`),
-            categories(ds$petloc$petloc_work)
-        )
-        expect_equivalent(
-            categories(ds$derivedarray$`petloc_work__1`),
-            categories(ds$petloc$petloc_work)
-        )
-    })
-
-    test_that("changing cat ids (values+metadata) first subvar", {
-        # check the first subvar
-        expect_equivalent(
-            as.vector(ds$derivedarray$`petloc_home__1`),
-            as.vector(ds$petloc$petloc_home)
-        )
-        expect_equivalent(
-            as.vector(ds$derivedarray$`petloc_home__1`, mode = "id"),
-            as.vector(ds$petloc$petloc_home, mode = "id")
-        )
-    })
-
-    test_that("changing cat ids (values+metadata) second subvar", {
-        # check the second subvar
-        expect_equivalent(
-            as.vector(ds$derivedarray$`petloc_work__1`),
-            as.vector(ds$petloc$petloc_work)
-        )
-        expect_equivalent(
-            as.vector(ds$derivedarray$`petloc_work__1`, mode = "id"),
-            as.vector(ds$petloc$petloc_work, mode = "id")
-        )
-    })
-
-    test_that("changing cat ids (values+metadata) whole array", {
-        # check the whole array
-        expect_equivalent(
-            as.vector(ds$derivedarray),
-            as.vector(ds$petloc)
-        )
-        expect_equivalent(
-            as.vector(ds$derivedarray, mode = "id"),
-            as.vector(ds$petloc, mode = "id")
-        )
-    })
-
-    # Test derive from categorical arrays that are stored as sparse categorical
-    #
-    # Make a factor that is overwhelmingly NA, but with some combos we want to
-    # collapse. Confirmed that this ratio is stored as sparse categorical, but
-    # if the definitions for what counts as sparse change, this might need to be
-    # changed to maintain coverage
-    # fac <- factor(
-    #     c(rep("A", 6), rep("B", 5), rep("C", 4),
-    #       rep("a", 3), rep("b", 2), rep("c", 1), rep(NA, 979))
-    # )
-    # first <- sample(fac, 1000)
-    # second <- sample(fac, 1000)
-    # df <- data.frame(
-    #     first = first,
-    #     second = second,
-    #     first_copy = first,
-    #     second_copy = second
+    # Revisit after https://www.pivotaltracker.com/n/projects/2172644/stories/186660623
+    # (reusable subvar codes) ships
+    # # reinstantiate the dataset so prior failures don't cloud current tests
+    # ds <- new_ds_with_derived_array()
+    #
+    # test_that("changing category names in metadata carries", {
+    #     existing <- names(categories(ds$petloc))
+    #     existing[1] <- "Kat"
+    #     existing[2] <- "Dogz"
+    #     names(categories(ds$petloc)) <- existing
+    #     ds <- refresh(ds) # must refresh to update the derived variable's metadata
+    #
+    #     expect_true(is.derived(ds$derivedarray))
+    #     expect_equivalent(
+    #         categories(ds$derivedarray),
+    #         categories(ds$petloc)
+    #     )
+    #     expect_equivalent(
+    #         categories(ds$derivedarray$`petloc_work__1`),
+    #         categories(ds$petloc$petloc_work)
+    #     )
+    #
+    #     # checking the petloc_work subvar since if the above tests failed,
+    #     # we know that petloc_home is broken
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray$`petloc_work__1`),
+    #         as.vector(ds$petloc$petloc_work)
+    #     )
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray$`petloc_work__1`, mode = "id"),
+    #         as.vector(ds$petloc$petloc_work, mode = "id")
+    #     )
+    # })
+    #
+    # # change category ids
+    # ds$petloc <- changeCategoryID(ds$petloc, 1, 10)
+    # ds <- refresh(ds) # must refresh to update the derived variable's metadata
+    #
+    # test_that("changing cat ids (values+metadata) metadata", {
+    #     expect_true(is.derived(ds$derivedarray))
+    #     expect_equivalent(
+    #         categories(ds$derivedarray),
+    #         categories(ds$petloc)
+    #     )
+    #     expect_equivalent(
+    #         categories(ds$derivedarray$`petloc_work__1`),
+    #         categories(ds$petloc$petloc_work)
+    #     )
+    #     expect_equivalent(
+    #         categories(ds$derivedarray$`petloc_work__1`),
+    #         categories(ds$petloc$petloc_work)
+    #     )
+    # })
+    #
+    # test_that("changing cat ids (values+metadata) first subvar", {
+    #     # check the first subvar
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray$`petloc_home__1`),
+    #         as.vector(ds$petloc$petloc_home)
+    #     )
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray$`petloc_home__1`, mode = "id"),
+    #         as.vector(ds$petloc$petloc_home, mode = "id")
+    #     )
+    # })
+    #
+    # test_that("changing cat ids (values+metadata) second subvar", {
+    #     # check the second subvar
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray$`petloc_work__1`),
+    #         as.vector(ds$petloc$petloc_work)
+    #     )
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray$`petloc_work__1`, mode = "id"),
+    #         as.vector(ds$petloc$petloc_work, mode = "id")
+    #     )
+    # })
+    #
+    # test_that("changing cat ids (values+metadata) whole array", {
+    #     # check the whole array
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray),
+    #         as.vector(ds$petloc)
+    #     )
+    #     expect_equivalent(
+    #         as.vector(ds$derivedarray, mode = "id"),
+    #         as.vector(ds$petloc, mode = "id")
+    #     )
+    # })
+    #
+    # # Test derive from categorical arrays that are stored as sparse categorical
+    # #
+    # # Make a factor that is overwhelmingly NA, but with some combos we want to
+    # # collapse. Confirmed that this ratio is stored as sparse categorical, but
+    # # if the definitions for what counts as sparse change, this might need to be
+    # # changed to maintain coverage
+    # # fac <- factor(
+    # #     c(rep("A", 6), rep("B", 5), rep("C", 4),
+    # #       rep("a", 3), rep("b", 2), rep("c", 1), rep(NA, 979))
+    # # )
+    # # first <- sample(fac, 1000)
+    # # second <- sample(fac, 1000)
+    # # df <- data.frame(
+    # #     first = first,
+    # #     second = second,
+    # #     first_copy = first,
+    # #     second_copy = second
+    # # )
+    # # # need to change categories to IDs, and then remove NAs
+    # # write.csv(df, "mocks/dataset-fixtures/sparse_ca.csv", row.names = FALSE)
+    #
+    # # we need to create with metadata to ensure that the categorical array is
+    # # stored as sparse categorical (if we use bind, then we have to figure out
+    # # how to trigger a cleanup which is not exposed to the API)
+    # ds <- createWithMetadataAndFile(
+    #     fromJSON(
+    #         datasetFixturePath("sparse_ca.json"),
+    #         simplifyVector = FALSE
+    #     ),
+    #     test_path(datasetFixturePath("sparse_ca.csv"))
     # )
-    # # need to change categories to IDs, and then remove NAs
-    # write.csv(df, "mocks/dataset-fixtures/sparse_ca.csv", row.names = FALSE)
-
-    # we need to create with metadata to ensure that the categorical array is
-    # stored as sparse categorical (if we use bind, then we have to figure out
-    # how to trigger a cleanup which is not exposed to the API)
-    ds <- createWithMetadataAndFile(
-        fromJSON(
-            datasetFixturePath("sparse_ca.json"),
-            simplifyVector = FALSE
-        ),
-        test_path(datasetFixturePath("sparse_ca.csv"))
-    )
-
-    test_that("combine on categorical array stored as sparse returns correct values", {
-        # the first categorical array is the same as the copies
-        first_copy_vals <- as.vector(ds$first_copy)
-        second_copy_vals <- as.vector(ds$second_copy)
-        expect_equal(as.vector(ds$cat_array$first), first_copy_vals)
-        expect_equal(as.vector(ds$cat_array$second), second_copy_vals)
-
-        # make our combined variable
-        ds$ca_combined <- combine(
-            ds$cat_array,
-            combinations = list(
-                list(
-                    name = "A",
-                    categories = c("A", "a")
-                ),
-                list(
-                    name = "B",
-                    categories = c("B", "b")
-                ),
-                list(
-                    name = "C",
-                    categories = c("C", "c")
-                )
-            )
-        )
-
-        # combine the values on the vector to compare with the combined variable
-        levels(first_copy_vals) <- c("A", "B", "C", "A", "B", "C")
-        levels(second_copy_vals) <- c("A", "B", "C", "A", "B", "C")
-
-        expect_equal(as.vector(ds$ca_combined$`first__1`), first_copy_vals)
-        expect_equal(as.vector(ds$ca_combined$`second__1`), second_copy_vals)
-
-        # and this might be clearer in a cube of the first subvar and the
-        # first_copy, this test is testing the same thing as above, with a cube
-        #
-        # we expect:
-        # first_copy
-        # first__1 A B C a b c
-        #      A 6 0 0 3 0 0
-        #      B 0 5 0 0 2 0
-        #      C 0 0 4 0 0 1
-        #
-        # we get:
-        # first_copy
-        # first__1 A B C a b c
-        #      A 6 5 4 3 0 0
-        #      B 0 0 0 0 2 0
-        #      C 0 0 0 0 0 1
-        dims <- list(
-            `first__1` = c("A", "B", "C"),
-            first_copy = c("A", "B", "C", "a", "b", "c")
-        )
-
-        expect_equivalent(
-            as.array(crtabs(~ ca_combined[["first__1"]] + first_copy, ds)),
-            cubify(
-                6, 0, 0, 3, 0, 0,
-                0, 5, 0, 0, 2, 0,
-                0, 0, 4, 0, 0, 1,
-                dims = dims
-            )
-        )
-    })
+    #
+    # test_that("combine on categorical array stored as sparse returns correct values", {
+    #     # the first categorical array is the same as the copies
+    #     first_copy_vals <- as.vector(ds$first_copy)
+    #     second_copy_vals <- as.vector(ds$second_copy)
+    #     expect_equal(as.vector(ds$cat_array$first), first_copy_vals)
+    #     expect_equal(as.vector(ds$cat_array$second), second_copy_vals)
+    #
+    #     # make our combined variable
+    #     ds$ca_combined <- combine(
+    #         ds$cat_array,
+    #         combinations = list(
+    #             list(
+    #                 name = "A",
+    #                 categories = c("A", "a")
+    #             ),
+    #             list(
+    #                 name = "B",
+    #                 categories = c("B", "b")
+    #             ),
+    #             list(
+    #                 name = "C",
+    #                 categories = c("C", "c")
+    #             )
+    #         )
+    #     )
+    #
+    #     # combine the values on the vector to compare with the combined variable
+    #     levels(first_copy_vals) <- c("A", "B", "C", "A", "B", "C")
+    #     levels(second_copy_vals) <- c("A", "B", "C", "A", "B", "C")
+    #
+    #     expect_equal(as.vector(ds$ca_combined$`first__1`), first_copy_vals)
+    #     expect_equal(as.vector(ds$ca_combined$`second__1`), second_copy_vals)
+    #
+    #     # and this might be clearer in a cube of the first subvar and the
+    #     # first_copy, this test is testing the same thing as above, with a cube
+    #     #
+    #     # we expect:
+    #     # first_copy
+    #     # first__1 A B C a b c
+    #     #      A 6 0 0 3 0 0
+    #     #      B 0 5 0 0 2 0
+    #     #      C 0 0 4 0 0 1
+    #     #
+    #     # we get:
+    #     # first_copy
+    #     # first__1 A B C a b c
+    #     #      A 6 5 4 3 0 0
+    #     #      B 0 0 0 0 2 0
+    #     #      C 0 0 0 0 0 1
+    #     dims <- list(
+    #         `first__1` = c("A", "B", "C"),
+    #         first_copy = c("A", "B", "C", "a", "b", "c")
+    #     )
+    #
+    #     expect_equivalent(
+    #         as.array(crtabs(~ ca_combined[["first__1"]] + first_copy, ds)),
+    #         cubify(
+    #             6, 0, 0, 3, 0, 0,
+    #             0, 5, 0, 0, 2, 0,
+    #             0, 0, 4, 0, 0, 1,
+    #             dims = dims
+    #         )
+    #     )
+    # })
 })