!34560 Update space_to_batch_nd tensor api doc

Merge pull request !34560 from zichun_ye/code_docs_space_to_batch_nd

mindspore/python/mindspore/common/tensor.py

@@ -2724,41 +2724,23 @@ class Tensor(Tensor_):
         r"""
         Divides spatial dimensions into blocks and combines the block size with the original batch.
 
-        Args:
-            block_shape (Union[list(int), tuple(int), int]): The block shape of dividing block with all value greater
-                than 1.
-            paddings (Union[tuple, list]): The padding values for spatial dimensions, containing 2 subtraction list.
-
-        Inputs:
-            - **input_x** (Tensor) - The input tensor. It must be a 4-D tensor on Ascend.
-
-        Outputs:
-            Tensor, the output tensor with the same data type as input.
-
-        Raises:
-            TypeError: If `block_shape` is not one of list, tuple, int.
-            TypeError: If `paddings` is neither list nor tuple.
-            ValueError: If `block_shape` is not one dimensional when `block_shape` is a list or tuple.
-            ValueError: If the length of `block_shape` is not 2 on Ascend.
-            ValueError: If shape of `paddings` is not (2, M), where M is the length of `block_shape`.
-            ValueError: If the element of `block_shape` is not an integer larger than 1.
-            ValueError: If the element of `paddings` is not an integer larger than 0.
+        Refer to :func:`mindspore.ops.space_to_batch_nd` for more detail.
 
         Supported Platforms:
-            ``Ascend`` ``GPU``
+            ``Ascend`` ``CPU``
 
-    Examples:
-        >>> import numpy as np
-        >>> from mindspore import Tensor
-        >>> block_shape = [2, 2]
-        >>> paddings = [[0, 0], [0, 0]]
-        >>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
-        >>> output = input_x.space_to_batch_nd(block_shape, paddings)
-        >>> print(output)
-        [[[[1.]]]
-         [[[2.]]]
-         [[[3.]]]
-         [[[4.]]]]
+        Examples:
+            >>> import numpy as np
+            >>> from mindspore import Tensor
+            >>> block_shape = [2, 2]
+            >>> paddings = [[0, 0], [0, 0]]
+            >>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
+            >>> output = input_x.space_to_batch_nd(block_shape, paddings)
+            >>> print(output)
+            [[[[1.]]]
+             [[[2.]]]
+             [[[3.]]]
+             [[[4.]]]]
         """
         return tensor_operator_registry.get('space_to_batch_nd')(block_shape, paddings)(self)