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PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

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# Copyright (c) 2025 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 paddle.base import core
__all__ = ["initial_seed"]
def initial_seed() -> int:
"""
Returns the initial seed for generating random numbers as a Python `int`.
Returns:
int: The 64-bit initial seed of the default generator on CPU place only.
Examples:
.. code-block:: pycon
>>> import paddle
>>> s = paddle.random.initial_seed()
"""
return core.default_cpu_generator().initial_seed()