fix CropAndResize doc

mindspore/ops/operations/image_ops.py

@@ -68,8 +68,8 @@ class CropAndResize(PrimitiveWithInfer):
         >>> IMAGE_WIDTH = 256
         >>> CHANNELS = 3
         >>> image = np.random.normal(size=[BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS]).astype(np.float32)
-        >>> boxes = np.random.uniform(shape=[NUM_BOXES, 4]).astype(np.float32)
-        >>> box_index = np.random.uniform(shape=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
+        >>> boxes = np.random.uniform(size=[NUM_BOXES, 4]).astype(np.float32)
+        >>> box_index = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
         >>> crop_size = np.array([24, 24]).astype(np.int32)
         >>> crop_and_resize = CropAndResizeNet(crop_size=Tensor(crop_size))
         >>> output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index))

tests/st/ops/ascend/test_aicpu_ops/test_crop_and_reszie.py

@@ -41,8 +41,8 @@ def test_net_float32():
     image_width = 256
     channels = 3
     image = np.random.normal(size=[batch_size, image_height, image_width, channels]).astype(np.float32)
-    boxes = np.random.uniform(shape=[num_boxes, 4]).astype(np.float32)
-    box_index = np.random.uniform(shape=[num_boxes], low=0, high=batch_size).astype(np.int32)
+    boxes = np.random.uniform(size=[num_boxes, 4]).astype(np.float32)
+    box_index = np.random.uniform(size=[num_boxes], low=0, high=batch_size).astype(np.int32)
     crop_size = np.array([24, 24]).astype(np.int32)
     net = Net(crop_size=Tensor(crop_size))
     output = net(Tensor(image), Tensor(boxes), Tensor(box_index))