Check dtype support of each array in resulting tuple in call_origin (#1457)

* Check dtype support of each array in resulting tuple in call_origin

* Use dpnp.linspace in test_histograms

Author: vlad-perevezentsev

dpnp/dpnp_utils/dpnp_algo_utils.pyx

@@ -201,7 +201,11 @@ def call_origin(function, *args, **kwargs):
         for res_origin in result:
             res = res_origin
             if isinstance(res_origin, numpy.ndarray):
-                res = dpnp_container.empty(res_origin.shape, dtype=res_origin.dtype, sycl_queue=exec_q)
+                if exec_q is not None:
+                    result_dtype = map_dtype_to_device(res_origin.dtype, exec_q.sycl_device)
+                else:
+                    result_dtype = res_origin.d_type
+                res = dpnp_container.empty(res_origin.shape, dtype=result_dtype, sycl_queue=exec_q)
                 copy_from_origin(res, res_origin)
             result_list.append(res)
 

tests/test_histograms.py

@@ -15,12 +15,12 @@ def teardown(self):
     def test_simple(self):
         n = 100
         v = dpnp.random.rand(n)
-        (a, b) = dpnp.histogram(v)
+        a, _ = dpnp.histogram(v)
         # check if the sum of the bins equals the number of samples
         numpy.testing.assert_equal(dpnp.sum(a, axis=0), n)
         # check that the bin counts are evenly spaced when the data is from
         # a linear function
-        (a, b) = dpnp.histogram(numpy.linspace(0, 10, 100))
+        a, _ = dpnp.histogram(dpnp.linspace(0, 10, 100))
         numpy.testing.assert_array_equal(a, 10)
 
     @pytest.mark.usefixtures("allow_fall_back_on_numpy")
@@ -67,7 +67,7 @@ def test_density(self):
 
         # Taken from a bug report from N. Becker on the numpy-discussion
         # mailing list Aug. 6, 2010.
-        counts, dmy = dpnp.histogram(
+        counts, _ = dpnp.histogram(
             [1, 2, 3, 4], [0.5, 1.5, numpy.inf], density=True
         )
         numpy.testing.assert_equal(counts, [0.25, 0])