fix numpy func name error (no attribute '_bitwise_xor') and add type …

…stubs for bitwise_xor parameter alias support

python/paddle/tensor/logic.py

@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, overload
 
 from typing_extensions import TypeGuard
 
@@ -49,6 +49,20 @@
 if TYPE_CHECKING:
     from paddle import Tensor
 
+    @overload
+    def bitwise_xor(
+        x: Tensor, y: Tensor, out: Tensor | None = None, name: str | None = None
+    ) -> Tensor: ...
+
+    def bitwise_xor(
+        *,
+        input: Tensor,
+        other: Tensor,
+        out: Tensor | None = None,
+        name: str | None = None,
+    ) -> Tensor: ...
+
+
 __all__ = []
 
 
@@ -1226,7 +1240,9 @@ def bitwise_xor(
 
     Args:
         x (Tensor): Input Tensor of ``bitwise_xor`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
+            Parameter alias: ``input``.
         y (Tensor): Input Tensor of ``bitwise_xor`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
+            Parameter alias: ``other``.
         out (Tensor|None, optional): Result of ``bitwise_xor`` . It is a N-D Tensor with the same data type of input Tensor. Default: None.
         name (str|None, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
@@ -1240,10 +1256,17 @@ def bitwise_xor(
             >>> import paddle
             >>> x = paddle.to_tensor([-5, -1, 1])
             >>> y = paddle.to_tensor([4,  2, -3])
+            >>> # Using original parameter name
             >>> res = paddle.bitwise_xor(x, y)
             >>> print(res)
             Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
             [-1, -3, -4])
+
+            >>> # Using Pytorch-compatible parameter name
+            >>> res = paddle.bitwise_xor(input=x, other=y)
+            >>> print(res)
+            Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [-1, -3, -4])
     """
     if in_dynamic_or_pir_mode() and out is None:
         return _C_ops.bitwise_xor(x, y)

test/legacy_test/test_zero_dim_binary_api.py

@@ -167,7 +167,7 @@ def test_dygraph_binary(self):
             # 1) x is 0D, y is 0D
             x_np = np.random.randint(-10, 10, [])
             y_np = np.random.randint(-10, 10, [])
-            out_np = eval(f'np.{api.__name__}(x_np, y_np)')
+            out_np = eval(f'np.{api.__name__.lstrip("_")}(x_np, y_np)')
 
             x = paddle.to_tensor(x_np)
             y = paddle.to_tensor(y_np)
@@ -179,7 +179,7 @@ def test_dygraph_binary(self):
             # 2) x is ND, y is 0D
             x_np = np.random.randint(-10, 10, [3, 5])
             y_np = np.random.randint(-10, 10, [])
-            out_np = eval(f'np.{api.__name__}(x_np, y_np)')
+            out_np = eval(f'np.{api.__name__.lstrip("_")}(x_np, y_np)')
 
             x = paddle.to_tensor(x_np)
             y = paddle.to_tensor(y_np)
@@ -191,7 +191,7 @@ def test_dygraph_binary(self):
             # 3) x is 0D , y is ND
             x_np = np.random.randint(-10, 10, [])
             y_np = np.random.randint(-10, 10, [3, 5])
-            out_np = eval(f'np.{api.__name__}(x_np, y_np)')
+            out_np = eval(f'np.{api.__name__.lstrip("_")}(x_np, y_np)')
 
             x = paddle.to_tensor(x_np)
             y = paddle.to_tensor(y_np)