random.seed(SEED) np.random.seed(SEED) torch.manual_seed(SEED)

这三行代码是用于设置随机种子的，目的是使得每次运行代码时，生成的随机数都是一样的，以保证实验的可重复性。其中，random.seed()和np.random.seed()分别用于设置Python自带的随机数生成器和numpy库的随机数生成器的种子，而torch.manual_seed()则用于设置PyTorch的随机数生成器的种子。在使用随机数的场景下，我们通常需要设置随机种子来确保实验结果的一致性。

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seed = 1 random.seed(seed) torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed)

These lines of code are used to set the random seed for various libraries and modules used in a Python program.

The `seed` variable is set to a value of 1. This value can be changed to any integer to set a different seed.

The `random.seed(seed)` function call sets the seed for the Python `random` module. This module is used to generate random numbers in Python.

The `torch.manual_seed(seed)` function call sets the seed for the PyTorch library. PyTorch is a popular deep learning library.

The `np.random.seed(seed)` function call sets the seed for the NumPy library. NumPy is a library for numerical computing in Python.

Finally, the `torch.cuda.manual_seed(seed)` function call sets the seed for the PyTorch CUDA backend. This is used for GPU acceleration in PyTorch.

Setting the random seed ensures that the random numbers generated during program execution are consistent across different runs. This is useful for debugging and testing purposes.

seed = 1234 np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed)

These lines of code set the random seed for three different libraries: numpy, PyTorch (CPU), and PyTorch (GPU). Setting the random seed ensures that the sequence of random numbers generated by these libraries is reproducible. This is useful when running experiments or training models, as it ensures that the results can be replicated.

The specific value used for the seed (1234 in this case) is arbitrary and could be any integer.