'bn.running_mean': torch.randn(3),
}
Apply module computation to the input with the specified parameters / buffers.
inp = torch.randn(5, 3)
output = functional_call(m, params_and_buffers, inp)
```
(Beta) Complex32 and Complex Convolutions in PyTorch
PyTorch today natively supports complex numbers, complex autograd, complex modules, and numerous complex operations, including linear algebra and Fast Fourier Transform (FFT) operators. Many libraries, including torchaudio and ESPNet, already make use of complex numbers in PyTorch, and PyTorch 1.12 further extends complex functionality with complex convolutions and the experimental complex32 (“complex half”) data type that enables half precision FFT operations. Due to the bugs in CUDA 11.3 package, we recommend using CUDA 11.6 package from wheels if you are using complex numbers.
(Beta) Forward-mode Automatic Differentiation