Fix op test error

tests/python/unittest/test_numpy_op.py

@@ -4087,80 +4087,6 @@ def hybrid_forward(self,F,a):
             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol) 
 
 
-
-# @with_seed()
-# @use_np
-# def test_np_diag():
-#     class TestDiag(HybridBlock):
-#         def __init__(self, k = 0):
-#             super(TestDiag, self).__init__()
-#             self._k  = k
-#             # necessary initializations
-
-#         def hybrid_forward(self, F, a):
-#             return F.np.diag(a, k = self._k)
-
-#     shapes = [(2,),5 , (1,5), (2,2), (2,5), (3,3), (4,3)] # test_shapes, remember to include zero-dim shape and zero-size shapes
-#     dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64] # remember to include all meaningful data types for the operator
-#     range_k = 6
-#     for hybridize, shape, dtype, in itertools.product([False, True], shapes, dtypes):
-#         # More for-loops for iterating through all other arguments
-#         # rtol atol values are for reference, may vary for different ops
-#         rtol = 1e-2 if dtype == np.float16 else 1e-3
-#         atol = 1e-4 if dtype == np.float16 else 1e-5
-
-#         for k in range(-range_k, range_k):
-#             test_diag = TestDiag(k)
-#             if hybridize:
-#                 test_diag.hybridize()
-
-#             # here the low and high could vary for different ops
-#             x = np.random.uniform(-1.0, 1.0, size=shape).astype(dtype)
-#             x.attach_grad()
-
-#             np_out = _np.diag(x.asnumpy(), k)
-#             with mx.autograd.record():
-#                 mx_out = test_diag(x)
-
-#             assert mx_out.shape == np_out.shape
-#             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-
-#             mx_out.backward()
-#             # Code to get reference backward value
-#             np_backward = 0
-#             if type(shape) == int:
-#                 np_backward = np.ones(shape)
-#             elif len(shape) < 2:
-#                 np_backward = np.ones(shape[0])
-#             else:
-#                 np_backward = np.zeros(shape)
-
-#                 h = shape[0]
-#                 w = shape[1]
-
-#                 if k>0:
-#                     w -= k
-#                 else:
-#                     h += k
-
-#                 s = min(w,h)
-
-#                 if s > 0:
-#                     if k >= 0:
-#                         for i in range(s):
-#                             np_backward[0+i][k+i] = 1
-#                     else:
-#                         for i in range(s):
-#                             np_backward[-k+i][0+i] = 1
-
-#             assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
-
-#             # Test imperative once again
-#             mx_out = np.diag(x, k)
-#             np_out = _np.diag(x.asnumpy(), k)
-#             assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-
-
 @with_seed()
 @use_np
 def test_np_rand():