update description

Author: vtavana

dpnp/dpnp_array.py

@@ -1317,21 +1317,34 @@ def transpose(self, *axes):
 
         For full documentation refer to :obj:`numpy.ndarray.transpose`.
 
+        Parameters
+        ----------
+        a : {dpnp.ndarray, usm_ndarray}
+            Input array.
+        axes : None, tuple or list of ints, n ints, optional
+
+          * ``None`` or no argument: reverses the order of the axes.
+          * tuple or list of ints: `i` in the `j`-th place in the tuple/list
+          means that the array’s `i`-th axis becomes the transposed
+          array’s `j`-th axis.
+          * n ints: same as an n-tuple/n-list of the same ints (this form is
+          intended simply as a “convenience” alternative to the tuple form).
+
         Returns
         -------
         y : dpnp.ndarray
             View of the array with its axes suitably permuted.
 
         See Also
         --------
-            :obj:`dpnp.transpose` : Equivalent function.
-            :obj:`dpnp.ndarray.ndarray.T` : Array property returning the array transposed.
-            :obj:`dpnp.ndarray.reshape` : Give a new shape to an array without changing its data.
+        :obj:`dpnp.transpose` : Equivalent function.
+        :obj:`dpnp.ndarray.ndarray.T` : Array property returning the array transposed.
+        :obj:`dpnp.ndarray.reshape` : Give a new shape to an array without changing its data.
 
         Examples
         --------
-        >>> import dpnp as dp
-        >>> a = dp.array([[1, 2], [3, 4]])
+        >>> import dpnp as np
+        >>> a = np.array([[1, 2], [3, 4]])
         >>> a
         array([[1, 2],
                [3, 4]])
@@ -1342,7 +1355,7 @@ def transpose(self, *axes):
         array([[1, 3],
                [2, 4]])
 
-        >>> a = dp.array([1, 2, 3, 4])
+        >>> a = np.array([1, 2, 3, 4])
         >>> a
         array([1, 2, 3, 4])
         >>> a.transpose()

dpnp/dpnp_iface_manipulation.py

@@ -1861,12 +1861,13 @@ def transpose(a, axes=None):
     ----------
     a : {dpnp.ndarray, usm_ndarray}
         Input array.
-    axes : tuple or list of ints, optional
+    axes : None, tuple or list of ints, optional
         If specified, it must be a tuple or list which contains a permutation
         of [0, 1, ..., N-1] where N is the number of axes of `a`.
         The `i`'th axis of the returned array will correspond to the axis
-        numbered ``axes[i]`` of the input. If not specified, defaults to
-        ``range(a.ndim)[::-1]``, which reverses the order of the axes.
+        numbered ``axes[i]`` of the input. If not specified or ``None``,
+        defaults to ``range(a.ndim)[::-1]``, which reverses the order of
+        the axes.
 
     Returns
     -------
@@ -1881,27 +1882,27 @@ def transpose(a, axes=None):
 
     Examples
     --------
-    >>> import dpnp as dp
-    >>> a = dp.array([[1, 2], [3, 4]])
+    >>> import dpnp as np
+    >>> a = np.array([[1, 2], [3, 4]])
     >>> a
     array([[1, 2],
            [3, 4]])
-    >>> dp.transpose(a)
+    >>> np.transpose(a)
     array([[1, 3],
            [2, 4]])
 
-    >>> a = dp.array([1, 2, 3, 4])
+    >>> a = np.array([1, 2, 3, 4])
     >>> a
     array([1, 2, 3, 4])
-    >>> dp.transpose(a)
+    >>> np.transpose(a)
     array([1, 2, 3, 4])
 
-    >>> a = dp.ones((1, 2, 3))
-    >>> dp.transpose(a, (1, 0, 2)).shape
+    >>> a = np.ones((1, 2, 3))
+    >>> np.transpose(a, (1, 0, 2)).shape
     (2, 1, 3)
 
-    >>> a = dp.ones((2, 3, 4, 5))
-    >>> dp.transpose(a).shape
+    >>> a = np.ones((2, 3, 4, 5))
+    >>> np.transpose(a).shape
     (5, 4, 3, 2)
 
     """

tests/test_manipulation.py

@@ -158,5 +158,17 @@ def test_none_axes(self, shape):
         na = numpy.ones(shape)
         da = dpnp.ones(shape)
 
+        assert_array_equal(numpy.transpose(na), dpnp.transpose(da))
+        assert_array_equal(numpy.transpose(na, None), dpnp.transpose(da, None))
+
+        # ndarray
         assert_array_equal(na.transpose(), da.transpose())
         assert_array_equal(na.transpose(None), da.transpose(None))
+
+    def test_ndarray_axes_n_int(self):
+        na = numpy.ones((1, 2, 3))
+        da = dpnp.array(na)
+
+        expected = na.transpose(1, 0, 2)
+        result = da.transpose(1, 0, 2)
+        assert_array_equal(expected, result)