add fft functions

Author: steff456

signatures/fft-functions/fft.md

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+# fft
+
+## NumPy
+
+```
+numpy.fft.fft(a, n=None, axis=-1, norm=None) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.fft(a, n=None, axis=- 1, norm=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.fft(a, n=None, axis=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.fft(a, n=None, axis=- 1, norm=None) → ndarray
+```
+
+## MXNet
+
+```
+mxnet.ndarray.contrib.fft(data=None, compute_size=_Null, out=None, name=None, **kwargs) → ndarray
+```
+
+## PyTorch
+
+```
+torch.fft.fft(input, n=None, dim=-1, norm=None) → Tensor
+```
+
+## TensorFlow
+
+```
+tf.signal.fft(input, name=None) → Tensor
+```

signatures/fft-functions/fft2.md

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+# fft2
+
+## NumPy
+
+```
+numpy.fft.fft2(a, s=None, axes=(-2, -1), norm=None) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.fft2(a, s=None, axes=(-2, -1), norm=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.fft2(a, s=None, axes=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.fft2(a, s=None, axes=(-2, -1), norm=None) → ndarray
+```
+
+## MXNet
+
+```
+
+```
+
+## PyTorch
+
+```
+torch.fft.fft2(input, s=None, dim=(-2, -1), norm=None) → Tensor
+```
+
+## TensorFlow
+
+```
+tf.signal.fft2d(input, name=None) → Tensor
+```

signatures/fft-functions/fftfreq.md

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+# fftfreq
+
+## NumPy
+
+```
+numpy.fft.fftfreq(n, d=1.0) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.fftfreq(n, d=1.0) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.fftfreq(n, d=1.0, chunks='auto') → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.fftfreq(n, d=1.0) → ndarray
+```
+
+## MXNet
+
+```
+
+```
+
+## PyTorch
+
+```
+torch.fft.fftfreq(n, d=1.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
+```
+
+## TensorFlow
+
+```
+
+```

signatures/fft-functions/fftn.md

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+# fftn
+
+## NumPy
+
+```
+numpy.fft.fftn(a, n=None, axis=-1, norm=None) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.fftn(a, n=None, axis=- 1, norm=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.fftn(a, s=None, axes=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.fftn(a, s=None, axes=None, norm=None) → ndarray
+```
+
+## MXNet
+
+```
+
+```
+
+## PyTorch
+
+```
+torch.fft.fftn(input, s=None, dim=None, norm=None) → Tensor
+```
+
+## TensorFlow
+
+```
+
+```

signatures/fft-functions/fftshift.md

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+# fftshift
+
+## NumPy
+
+```
+numpy.fft.fftshift(x, axes=None) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.fftshift(x, axes=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.fftshift(x, axes=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.fftshift(x, axes=None) → ndarray
+```
+
+## MXNet
+
+```
+
+```
+
+## PyTorch
+
+```
+torch.fft.fftshift(input, dim=None) → Tensor
+```
+
+## TensorFlow
+
+```
+
+```

signatures/fft-functions/hfft.md

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+# hfft
+
+## NumPy
+
+```
+numpy.fft.hfft(a, n=None, axis=-1, norm=None) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.hfft(a, n=None, axis=- 1, norm=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.hfft(a, n=None, axis=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.hfft(a, n=None, axis=- 1, norm=None) → ndarray
+```
+
+## MXNet
+
+```
+
+```
+
+## PyTorch
+
+```
+torch.fft.hfft(input, n=None, dim=-1, norm=None) → Tensor
+```
+
+## TensorFlow
+
+```
+
+```

signatures/fft-functions/ifft.md

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+# ifft
+
+## NumPy
+
+```
+numpy.fft.ifft(a, n=None, axis=-1, norm=None) → complex ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.ifft(a, n=None, axis=- 1, norm=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.ifft(a, n=None, axis=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.ifft(a, n=None, axis=- 1, norm=None) → ndarray
+```
+
+## MXNet
+
+```
+mxnet.ndarray.contrib.ifft(data=None, compute_size=_Null, out=None, name=None, **kwargs) → ndarray
+```
+
+## PyTorch
+
+```
+torch.fft.ifft(input, n=None, dim=-1, norm=None) → Tensor
+```
+
+## TensorFlow
+
+```
+tf.signal.ifft(input, name=None) → Tensor
+```

signatures/fft-functions/ifft2.md

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+# ifft2
+
+## NumPy
+
+```
+numpy.fft.ifft2(a, s=None, axes=(-2, -1), norm=None) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.ifft2(a, s=None, axes=(-2, -1), norm=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.ifft2(a, s=None, axes=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.ifft2(a, s=None, axes=(-2, -1), norm=None) → ndarray
+```
+
+## MXNet
+
+```
+
+```
+
+## PyTorch
+
+```
+torch.fft.ifft2(input, s=None, dim=(-2, -1), norm=None) → Tensor
+```
+
+## TensorFlow
+
+```
+tf.signal.ifft2d(input, name=None) → Tensor
+```

signatures/fft-functions/ifftn.md

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+# ifftn
+
+## NumPy
+
+```
+numpy.fft.ifftn(a, s=None, axes=None, norm=None) → ndarray
+```
+
+## CuPy
+
+```
+cupy.fft.ifftn(a, s=None, axes=None, norm=None) → ndarray
+```
+
+## dask.array
+
+```
+dask.array.fft.ifftn(a, s=None, axes=None) → ndarray
+```
+
+## JAX
+
+```
+jax.numpy.fft.ifftn(a, s=None, axes=None, norm=None) → ndarray
+```
+
+## MXNet
+
+```
+
+```
+
+## PyTorch
+
+```
+torch.fft.ifftn(input, s=None, dim=None, norm=None) → Tensor
+```
+
+## TensorFlow
+
+```
+
+```