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.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import paddle
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from paddle import Tensor
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from paddle.framework import (
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in_dynamic_mode,
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)
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def _check_out_status(
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out: Tensor | tuple[Tensor, Tensor] | list[Tensor],
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expect_multiple: bool = False,
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):
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if out is None:
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return
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if not in_dynamic_mode():
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raise RuntimeError(
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"Using `out` static graph CINN backend is currently not supported. Directly return the tensor tuple instead.\n"
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)
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if expect_multiple:
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if not isinstance(out, (tuple, list)) or len(out) != 2:
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raise TypeError(
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f"Expected a list or tuple of two tensors, got {type(out)} instead."
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)
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if not (
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isinstance(out[0], paddle.Tensor)
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and isinstance(out[1], paddle.Tensor)
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):
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raise TypeError(
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f"Expected Tensor type in the tuple/list, got ({type(out[0])}, {type(out[1])}) instead."
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)
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else:
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if not isinstance(out, paddle.Tensor):
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raise TypeError(f"Expected a Tensor, got {type(out)} instead.")
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