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Showing content from https://docs.pytorch.org/docs/stable/generated/torch.Tensor.numpy.html below:

torch.Tensor.numpy — PyTorch 2.7 documentation

Tensor.numpy(*, force=False) → numpy.ndarray¶

Returns the tensor as a NumPy ndarray.

If force is False (the default), the conversion is performed only if the tensor is on the CPU, does not require grad, does not have its conjugate bit set, and is a dtype and layout that NumPy supports. The returned ndarray and the tensor will share their storage, so changes to the tensor will be reflected in the ndarray and vice versa.

If force is True this is equivalent to calling t.detach().cpu().resolve_conj().resolve_neg().numpy(). If the tensor isn’t on the CPU or the conjugate or negative bit is set, the tensor won’t share its storage with the returned ndarray. Setting force to True can be a useful shorthand.

Parameters

force (bool) – if True, the ndarray may be a copy of the tensor instead of always sharing memory, defaults to False.

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