diff --git a/numba_cuda/numba/cuda/tests/nrt/mock_numpy.py b/numba_cuda/numba/cuda/tests/nrt/mock_numpy.py
index bc3dfba..a59df57 100644
--- a/numba_cuda/numba/cuda/tests/nrt/mock_numpy.py
+++ b/numba_cuda/numba/cuda/tests/nrt/mock_numpy.py
@@ -1,9 +1,8 @@
-import numpy as np
-
 from numba.core import errors, types
 from numba.core.extending import overload
 from numba.np.arrayobj import (_check_const_str_dtype, is_nonelike,
-                               ty_parse_dtype, ty_parse_shape, numpy_empty_nd)
+                               ty_parse_dtype, ty_parse_shape, numpy_empty_nd,
+                               numpy_empty_like_nd)
 
 
 # Typical tests for allocation use array construction (e.g. np.zeros, np.empty,
@@ -48,12 +47,20 @@ def impl(shape, dtype):
 
 
 @overload(cuda_empty_like)
-def ol_cuda_empty_like(a, dtype=None):
-    _check_const_str_dtype("zeros_like", dtype)
-
-    # NumPy uses 'a' as the arg name for the array-like
-    def impl(a, dtype=None):
-        arr = np.empty_like(a, dtype=dtype)
-        arr._zero_fill()
-        return arr
+def ol_cuda_empty_like(arr):
+
+    if isinstance(arr, types.Array):
+        nb_dtype = arr.dtype
+    else:
+        nb_dtype = arr
+
+    if isinstance(arr, types.Array):
+        layout = arr.layout if arr.layout != 'A' else 'C'
+        retty = arr.copy(dtype=nb_dtype, layout=layout, readonly=False)
+    else:
+        retty = types.Array(nb_dtype, 0, 'C')
+
+    def impl(arr):
+        dtype = None
+        return numpy_empty_like_nd(arr, dtype, retty)
     return impl