chore: import upstream snapshot with attribution

python/paddle/tensor/logic.py

@@ -0,0 +1,878 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, TypeGuard
+
+import paddle
+from paddle import _C_ops
+from paddle._C_ops import (  # noqa: F401
+    allclose,
+    bitwise_and,
+    bitwise_and_,
+    bitwise_not,
+    bitwise_not_,
+    bitwise_or,
+    bitwise_or_,
+    bitwise_xor,
+    bitwise_xor_,
+    greater_than,
+    isclose,
+    logical_and,
+    logical_not,
+    logical_or,
+    logical_xor,
+)
+from paddle.tensor.creation import full
+from paddle.tensor.math import broadcast_shape
+from paddle.utils.decorator_utils import (
+    param_one_alias,
+    param_two_alias,
+)
+from paddle.utils.inplace_utils import inplace_apis_in_dygraph_only
+
+from ..base.data_feeder import check_type, check_variable_and_dtype
+from ..common_ops_import import Variable
+from ..framework import (
+    LayerHelper,
+    in_dynamic_mode,
+    in_dynamic_or_pir_mode,
+    in_pir_mode,
+)
+
+if TYPE_CHECKING:
+    from paddle import Tensor
+
+__all__ = []
+
+
+@inplace_apis_in_dygraph_only
+def logical_and_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``logical_and`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_logical_and`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.logical_and_(x, y)
+
+
+@inplace_apis_in_dygraph_only
+def logical_or_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``logical_or`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_logical_or`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.logical_or_(x, y)
+
+
+@inplace_apis_in_dygraph_only
+def logical_xor_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``logical_xor`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_logical_xor`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.logical_xor_(x, y)
+
+
+@inplace_apis_in_dygraph_only
+def logical_not_(x: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``logical_not`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_logical_not`.
+    """
+    if in_dynamic_mode():
+        return _C_ops.logical_not_(x)
+
+
+def is_empty(x: Tensor, name: str | None = None) -> Tensor:
+    """
+
+    Test whether a Tensor is empty.
+
+    Args:
+        x (Tensor): The Tensor to be tested.
+        name (str|None, optional): The default value is ``None`` . Normally users don't have to set this parameter. For more information, please refer to :ref:`api_guide_Name` .
+
+    Returns:
+        Tensor: A bool scalar Tensor. True if 'x' is an empty Tensor.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> input = paddle.rand(shape=[4, 32, 32], dtype='float32')
+            >>> res = paddle.is_empty(x=input)
+            >>> print(res)
+            Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
+            False)
+
+    """
+    if in_dynamic_mode():
+        return _C_ops.is_empty(x)
+
+    check_variable_and_dtype(
+        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'is_empty'
+    )
+    check_type(name, "name", (str, type(None)), "is_empty")
+    if in_pir_mode():
+        return _C_ops.is_empty(x)
+    else:
+        helper = LayerHelper("is_empty", **locals())
+        cond = helper.create_variable_for_type_inference(dtype='bool')
+        cond.stop_gradient = True
+        helper.append_op(
+            type='is_empty', inputs={'X': [x]}, outputs={'Out': [cond]}
+        )
+        return cond
+
+
+def equal_all(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    """
+    Returns the truth value of :math:`x == y`. True if two inputs have the same elements, False otherwise.
+
+    Note:
+        The output has no gradient.
+
+    Args:
+        x(Tensor): Tensor, data type is bool, float32, float64, int32, int64.
+        y(Tensor): Tensor, data type is bool, float32, float64, int32, int64.
+        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`.
+
+    Returns:
+        Tensor: output Tensor, data type is bool, value is [False] or [True].
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([1, 2, 3])
+            >>> y = paddle.to_tensor([1, 2, 3])
+            >>> z = paddle.to_tensor([1, 4, 3])
+            >>> result1 = paddle.equal_all(x, y)
+            >>> print(result1)
+            Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
+            True)
+            >>> result2 = paddle.equal_all(x, z)
+            >>> print(result2)
+            Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
+            False)
+    """
+    if in_dynamic_or_pir_mode():
+        return _C_ops.equal_all(x, y)
+    else:
+        helper = LayerHelper("equal_all", **locals())
+        out = helper.create_variable_for_type_inference(dtype='bool')
+        helper.append_op(
+            type='equal_all',
+            inputs={'X': [x], 'Y': [y]},
+            outputs={'Out': [out]},
+        )
+        return out
+
+
+@param_two_alias(["x", "input"], ["y", "other"])
+def equal(
+    x: Tensor, y: Tensor, name: str | None = None, *, out: Tensor | None = None
+) -> Tensor:
+    """
+
+    This layer returns the truth value of :math:`x == y` elementwise.
+
+    Note:
+        The output has no gradient.
+
+    Args:
+        x (Tensor): Tensor, data type is bool, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            alias: ``input``
+        y (Tensor): Tensor, data type is bool, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            alias: ``other``
+        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`.
+        out (Tensor, optional): Output tensor. If provided, the result will be stored in this tensor.
+
+    Returns:
+        Tensor: output Tensor, it's shape is the same as the input's Tensor,
+        and the data type is bool. The result of this op is stop_gradient.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([1, 2, 3])
+            >>> y = paddle.to_tensor([1, 3, 2])
+            >>> result1 = paddle.equal(x, y)
+            >>> print(result1)
+            Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
+            [True , False, False])
+    """
+    if not isinstance(
+        y, (int, bool, float, Variable, complex, paddle.pir.Value)
+    ):
+        raise TypeError(
+            f"Type of input args must be float, bool, complex, int or Tensor, but received type {type(y)}"
+        )
+    if not isinstance(y, (Variable, paddle.pir.Value, complex)):
+        y = full(shape=[], dtype=x.dtype, fill_value=y)
+
+    if isinstance(y, complex):
+        # full not support for complex yet
+        y = paddle.to_tensor(y)
+
+    if in_dynamic_or_pir_mode():
+        return _C_ops.equal(x, y, out=out)
+    else:
+        check_variable_and_dtype(
+            x,
+            "x",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "equal",
+        )
+        check_variable_and_dtype(
+            y,
+            "y",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "equal",
+        )
+        helper = LayerHelper("equal", **locals())
+        out = helper.create_variable_for_type_inference(dtype='bool')
+        out.stop_gradient = True
+        helper.append_op(
+            type='equal',
+            inputs={'X': [x], 'Y': [y]},
+            outputs={'Out': [out]},
+        )
+        return out
+
+
+@inplace_apis_in_dygraph_only
+def equal_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``equal`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_equal`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_or_pir_mode():
+        return _C_ops.equal_(x, y)
+
+
+@param_two_alias(["x", "input"], ["y", "other"])
+def greater_equal(
+    x: Tensor, y: Tensor, name: str | None = None, *, out: Tensor | None = None
+) -> Tensor:
+    """
+    Returns the truth value of :math:`x >= y` elementwise, which is equivalent function to the overloaded operator `>=`.
+
+    Note:
+        The output has no gradient.
+
+    Args:
+        x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``input``.
+        y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``other``.
+        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`.
+        out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None.
+    Returns:
+        Tensor: The output shape is same as input :attr:`x`. The output data type is bool.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([1, 2, 3])
+            >>> y = paddle.to_tensor([1, 3, 2])
+            >>> result1 = paddle.greater_equal(x, y)
+            >>> print(result1)
+            Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
+            [True , False, True ])
+    """
+    if in_dynamic_or_pir_mode():
+        return _C_ops.greater_equal(x, y, out=out)
+    else:
+        check_variable_and_dtype(
+            x,
+            "x",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "greater_equal",
+        )
+        check_variable_and_dtype(
+            y,
+            "y",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "greater_equal",
+        )
+        helper = LayerHelper("greater_equal", **locals())
+        out = helper.create_variable_for_type_inference(dtype='bool')
+        out.stop_gradient = True
+        helper.append_op(
+            type='greater_equal',
+            inputs={'X': [x], 'Y': [y]},
+            outputs={'Out': [out]},
+        )
+        return out
+
+
+@inplace_apis_in_dygraph_only
+def greater_equal_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``greater_equal`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_greater_equal`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.greater_equal_(x, y)
+
+
+@inplace_apis_in_dygraph_only
+@param_two_alias(["x", "input"], ["y", "other"])
+def greater_than_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``greater_than`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_greater_than`.
+    """
+    if not isinstance(y, paddle.Tensor):
+        y = paddle.to_tensor(y, dtype=x.dtype)
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.greater_than_(x, y)
+
+
+@param_two_alias(["x", "input"], ["y", "other"])
+def less_equal(
+    x: Tensor, y: Tensor, name: str | None = None, *, out: Tensor | None = None
+) -> Tensor:
+    """
+    Returns the truth value of :math:`x <= y` elementwise, which is equivalent function to the overloaded operator `<=`.
+
+    Note:
+        The output has no gradient.
+
+    Args:
+        x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``input``.
+        y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``other``.
+        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`.
+        out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None.
+
+    Returns:
+        Tensor: The output shape is same as input :attr:`x`. The output data type is bool.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([1, 2, 3])
+            >>> y = paddle.to_tensor([1, 3, 2])
+            >>> result1 = paddle.less_equal(x, y)
+            >>> print(result1)
+            Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
+            [True , True , False])
+    """
+    if in_dynamic_or_pir_mode():
+        return _C_ops.less_equal(x, y, out=out)
+    else:
+        check_variable_and_dtype(
+            x,
+            "x",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "less_equal",
+        )
+        check_variable_and_dtype(
+            y,
+            "y",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "less_equal",
+        )
+        helper = LayerHelper("less_equal", **locals())
+        out = helper.create_variable_for_type_inference(dtype='bool')
+        out.stop_gradient = True
+        helper.append_op(
+            type='less_equal',
+            inputs={'X': [x], 'Y': [y]},
+            outputs={'Out': [out]},
+        )
+        return out
+
+
+@inplace_apis_in_dygraph_only
+def less_equal_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``less_equal`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_less_equal`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.less_equal_(x, y)
+
+
+@param_two_alias(["x", "input"], ["y", "other"])
+def less_than(
+    x: Tensor, y: Tensor, name: str | None = None, *, out: Tensor | None = None
+) -> Tensor:
+    """
+    Returns the truth value of :math:`x < y` elementwise, which is equivalent function to the overloaded operator `<`.
+
+    Note:
+        The output has no gradient.
+
+    Args:
+        x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``input``
+        y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``other``
+        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`.
+        out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None.
+
+    Returns:
+        Tensor: The output shape is same as input :attr:`x`. The output data type is bool.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([1, 2, 3])
+            >>> y = paddle.to_tensor([1, 3, 2])
+            >>> result1 = paddle.less_than(x, y)
+            >>> print(result1)
+            Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
+            [False, True , False])
+    """
+    if in_dynamic_or_pir_mode():
+        return _C_ops.less_than(x, y, out=out)
+    else:
+        check_variable_and_dtype(
+            x,
+            "x",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "less_than",
+        )
+        check_variable_and_dtype(
+            y,
+            "y",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "less_than",
+        )
+        helper = LayerHelper("less_than", **locals())
+        out = helper.create_variable_for_type_inference(dtype='bool')
+        out.stop_gradient = True
+
+        helper.append_op(
+            type='less_than',
+            inputs={'X': [x], 'Y': [y]},
+            outputs={'Out': [out]},
+        )
+        return out
+
+
+@inplace_apis_in_dygraph_only
+def less_than_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``less_than`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_less_than`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.less_than_(x, y)
+
+
+@inplace_apis_in_dygraph_only
+def less_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``less_`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_less`.
+    """
+
+    # Directly call less_than_ API
+    return less_than_(x, y, name)
+
+
+@param_two_alias(["x", "input"], ["y", "other"])
+def not_equal(
+    x: Tensor, y: Tensor, name: str | None = None, *, out: Tensor | None = None
+) -> Tensor:
+    """
+    Returns the truth value of :math:`x != y` elementwise, which is equivalent function to the overloaded operator `!=`.
+
+    Note:
+        The output has no gradient.
+
+    Args:
+        x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``input``.
+        y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+            Alias: ``other``.
+        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`.
+        out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None.
+
+    Returns:
+        Tensor: The output shape is same as input :attr:`x`. The output data type is bool.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([1, 2, 3])
+            >>> y = paddle.to_tensor([1, 3, 2])
+            >>> result1 = paddle.not_equal(x, y)
+            >>> print(result1)
+            Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
+            [False, True , True ])
+    """
+    if in_dynamic_or_pir_mode():
+        return _C_ops.not_equal(x, y, out=out)
+    else:
+        check_variable_and_dtype(
+            x,
+            "x",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "not_equal",
+        )
+        check_variable_and_dtype(
+            y,
+            "y",
+            [
+                "bool",
+                "float16",
+                "float32",
+                "float64",
+                "uint8",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "uint16",
+                "complex64",
+                "complex128",
+            ],
+            "not_equal",
+        )
+        helper = LayerHelper("not_equal", **locals())
+        out = helper.create_variable_for_type_inference(dtype='bool')
+        out.stop_gradient = True
+
+        helper.append_op(
+            type='not_equal',
+            inputs={'X': [x], 'Y': [y]},
+            outputs={'Out': [out]},
+        )
+        return out
+
+
+@inplace_apis_in_dygraph_only
+def not_equal_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``not_equal`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_not_equal`.
+    """
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError(
+            f"The shape of broadcast output {out_shape} is different from that of inplace tensor {x.shape} in the Inplace operation."
+        )
+    if in_dynamic_mode():
+        return _C_ops.not_equal_(x, y)
+
+
+@param_one_alias(["x", "obj"])
+def is_tensor(x: Any) -> TypeGuard[Tensor]:
+    """
+
+    Tests whether input object is a paddle.Tensor.
+
+    .. note::
+        Alias Support: The parameter name ``obj`` can be used as an alias for ``x``.
+        For example, ``is_tensor(obj=tensor_x)`` is equivalent to ``is_tensor(x=tensor_x)``.
+
+    Args:
+        x (object): Object to test. alias: ``obj``.
+
+    Returns:
+        A boolean value. True if ``x`` is a paddle.Tensor, otherwise False.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> input1 = paddle.rand(shape=[2, 3, 5], dtype='float32')
+            >>> check = paddle.is_tensor(input1)
+            >>> print(check)
+            True
+
+            >>> input3 = [1, 4]
+            >>> check = paddle.is_tensor(input3)
+            >>> print(check)
+            False
+
+    """
+    if in_dynamic_or_pir_mode():
+        return isinstance(x, (paddle.Tensor, paddle.pir.Value))
+    else:
+        return isinstance(x, Variable)
+
+
+def __rand__(x: Tensor, y: int | bool):
+    if isinstance(y, (int, bool)):
+        y_tensor = paddle.to_tensor(y, dtype=x.dtype)
+        return bitwise_and(y_tensor, x)
+    else:
+        raise TypeError(
+            f"unsupported operand type(s) for |: '{type(y).__name__}' and 'Tensor'"
+        )
+
+
+def __ror__(
+    x: Tensor,
+    y: int | bool,
+    out: Tensor | None = None,
+    name: str | None = None,
+) -> Tensor:
+    if isinstance(y, (int, bool)):
+        y = paddle.to_tensor(y, dtype=x.dtype)
+        return bitwise_or(y, x, out=out, name=name)
+    else:
+        raise TypeError(
+            f"unsupported operand type(s) for |: '{type(y).__name__}' and 'Tensor'"
+        )
+
+
+def __rxor__(
+    x: Tensor,
+    y: int | bool,
+    out: Tensor | None = None,
+    name: str | None = None,
+) -> Tensor:
+    if isinstance(y, (int, bool)):
+        y = paddle.to_tensor(y, dtype=x.dtype)
+        return bitwise_xor(y, x, out=out, name=name)
+    else:
+        raise TypeError(
+            f"unsupported operand type(s) for |: '{type(y).__name__}' and 'Tensor'"
+        )
+
+
+def bitwise_invert(
+    x: Tensor, out: Tensor | None = None, name: str | None = None
+) -> Tensor:
+    r"""
+    Apply ``bitwise_not`` (bitwise inversion) on Tensor ``x``.
+
+    This is an alias to the ``paddle.bitwise_not`` function.
+
+    .. math::
+        Out = \sim X
+
+    Note:
+        ``paddle.bitwise_invert`` is functionally equivalent to ``paddle.bitwise_not``.
+
+    Args:
+        x (Tensor): Input Tensor of ``bitwise_invert``. It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
+        out (Tensor|None, optional): Result of ``bitwise_invert``. It is a N-D Tensor with the same data type as the input Tensor. Default: None.
+        name (str|None, optional): The default value is None. This property is typically not set by the user.
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Tensor: Result of ``bitwise_invert``. It is a N-D Tensor with the same data type as the input Tensor.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> x = paddle.to_tensor([-5, -1, 1])
+            >>> res = x.bitwise_invert()
+            >>> print(res)
+            Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [ 4,  0, -2])
+
+    """
+    # Directly call bitwise_not for the implementation
+    return bitwise_not(x, out=out, name=name)
+
+
+@inplace_apis_in_dygraph_only
+def bitwise_invert_(x: Tensor, name: str | None = None) -> Tensor:
+    r"""
+    Inplace version of ``bitwise_invert`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_bitwise_invert_`.
+    """
+    # Directly call bitwise_not_ for the implementation
+    return bitwise_not_(x, name=name)