Explicitly skip test_resume with tensorstore

cubed/tests/test_executor_features.py

@@ -1,4 +1,3 @@
-import contextlib
 import os
 import platform
 import re
@@ -21,6 +20,7 @@
 from cubed.diagnostics.tqdm import TqdmProgressBar
 from cubed.primitive.blockwise import apply_blockwise
 from cubed.runtime.create import create_executor
+from cubed.storage.backend import backend_storage_name
 from cubed.tests.utils import (
     ALL_EXECUTORS,
     MAIN_EXECUTORS,
@@ -190,6 +190,10 @@ def func(a):
         b.compute(executor=executor, callbacks=[mem_warn])
 
 
+@pytest.mark.skipif(
+    backend_storage_name() == "tensorstore",
+    reason="tensorstore does not support resume",
+)
 def test_resume(spec, executor):
     if executor.name == "beam":
         pytest.skip(f"{executor.name} executor does not support resume")
@@ -207,24 +211,17 @@ def test_resume(spec, executor):
     num_created_arrays = 1  # c
     assert task_counter.value == num_created_arrays + 4
 
-    if not hasattr(c.zarray, "nchunks_initialized"):
-        # We expect resume to fail if there is no 'nchunks_initialized' property on the Zarr array
-        cm = pytest.raises(NotImplementedError)
-    else:
-        cm = contextlib.nullcontext()
-
-    with cm:
-        # since c has already been computed, when computing d only 4 tasks are run, instead of 8
-        task_counter = TaskCounter()
-        d.compute(
-            executor=executor,
-            callbacks=[task_counter],
-            optimize_graph=False,
-            resume=True,
-        )
-        # the create arrays tasks are run again, even though they exist
-        num_created_arrays = 2  # c, d
-        assert task_counter.value == num_created_arrays + 4
+    # since c has already been computed, when computing d only 4 tasks are run, instead of 8
+    task_counter = TaskCounter()
+    d.compute(
+        executor=executor,
+        callbacks=[task_counter],
+        optimize_graph=False,
+        resume=True,
+    )
+    # the create arrays tasks are run again, even though they exist
+    num_created_arrays = 2  # c, d
+    assert task_counter.value == num_created_arrays + 4
 
 
 @pytest.mark.parametrize("compute_arrays_in_parallel", [True, False])