Add dpnp.iterable function (#2208)

There was `__iter__` method of dpnp.ndarray implemented in scope of
#2206.

While this PR proposes to add implementation of `dpnp.iterable` function
which is intended to check if the object (including dpnp.ndarray) can be
iterated over.
The implementation leverages on NumPy call, which is based on attempt to
pass the object to `iter` function.

Author: antonwolfy

dpnp/dpnp_iface_indexing.py

@@ -69,6 +69,7 @@
     "fill_diagonal",
     "flatnonzero",
     "indices",
+    "iterable",
     "ix_",
     "mask_indices",
     "ndindex",
@@ -1033,6 +1034,47 @@ def indices(
     return res
 
 
+def iterable(y):
+    """
+    Check whether or not an object can be iterated over.
+
+    For full documentation refer to :obj:`numpy.iterable`.
+
+    Parameters
+    ----------
+    y : object
+        Input object.
+
+    Returns
+    -------
+    out : bool
+        Return ``True`` if the object has an iterator method or is a sequence
+        and ``False`` otherwise.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> np.iterable([1, 2, 3])
+    True
+    >>> np.iterable(2)
+    False
+
+    In most cases, the results of ``np.iterable(obj)`` are consistent with
+    ``isinstance(obj, collections.abc.Iterable)``. One notable exception is
+    the treatment of 0-dimensional arrays:
+
+    >>> from collections.abc import Iterable
+    >>> a = np.array(1.0)  # 0-dimensional array
+    >>> isinstance(a, Iterable)
+    True
+    >>> np.iterable(a)
+    False
+
+    """
+
+    return numpy.iterable(y)
+
+
 def ix_(*args):
     """Construct an open mesh from multiple sequences.
 

dpnp/tests/test_indexing.py

@@ -337,6 +337,14 @@ def test_ix_error(self, xp, shape):
         assert_raises(ValueError, xp.ix_, xp.ones(shape))
 
 
+class TestIterable:
+    @pytest.mark.parametrize("data", [[1.0], [2, 3]])
+    def test_basic(self, data):
+        a = numpy.array(data)
+        ia = dpnp.array(a)
+        assert dpnp.iterable(ia) == numpy.iterable(a)
+
+
 @pytest.mark.parametrize(
     "shape", [[1, 2, 3], [(1, 2, 3)], [(3,)], [3], [], [()], [0]]
 )