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Co-authored-by: Anton <[email protected]>

Author: AlexanderKalistratov

Diff for: dpnp/dpnp_iface_statistics.py

@@ -418,18 +418,19 @@ def convolve(a, v, mode="full", method="auto"):
 
         Note: Use of the FFT convolution on input containing NAN or INF
         will lead to the entire output being NAN or INF.
-        Use method='direct' when your input contains NAN or INF values.
+        Use ``method='direct'`` when your input contains NAN or INF values.
 
         Default: ``'auto'``.
 
     Returns
     -------
-    out : ndarray
+    out : dpnp.ndarray
         Discrete, linear convolution of `a` and `v`.
 
     See Also
     --------
     :obj:`dpnp.correlate` : Cross-correlation of two 1-dimensional sequences.
+
     Notes
     -----
     The discrete convolution operation is defined as
@@ -471,6 +472,7 @@ def convolve(a, v, mode="full", method="auto"):
 
     >>> np.convolve(a, v, 'valid')
     array([2.5], dtype=float32)
+
     """
 
     dpnp.check_supported_arrays_type(a, v)

Diff for: dpnp/tests/test_statistics.py

@@ -282,8 +282,8 @@ def test_convolve_shape_error(self, a, v):
 
     @pytest.mark.parametrize("size", [2, 10**1, 10**2, 10**3, 10**4, 10**5])
     def test_convolve_different_sizes(self, size):
-        a = numpy.random.rand(size).astype(numpy.float32)
-        v = numpy.random.rand(size // 2).astype(numpy.float32)
+        a = generate_random_numpy_array(size, dtype=numpy.float32, low=0, high=1)
+        v = generate_random_numpy_array(size // 2, dtype=numpy.float32, low=0, high=1)
 
         ad = dpnp.array(a)
         vd = dpnp.array(v)

Diff for: dpnp/tests/third_party/cupy/math_tests/test_misc.py

@@ -531,7 +531,6 @@ def test_heaviside_nan_inf(self, xp, dtype_1, dtype_2):
         }
     )
 )
-# @pytest.mark.skip("convolve() is not implemented yet")
 class TestConvolveShapeCombination:
 
     @testing.for_all_dtypes(no_float16=True)
@@ -542,7 +541,6 @@ def test_convolve(self, xp, dtype):
         return xp.convolve(a, b, mode=self.mode)
 
 
-# @pytest.mark.skip("convolve() is not implemented yet")
 @pytest.mark.parametrize("mode", ["valid", "same", "full"])
 class TestConvolve: