---
title: "realToHermiteanFFT(_:axesTensor:descriptor:name:)"
framework: metalperformanceshadersgraph
role: symbol
role_heading: Instance Method
path: "metalperformanceshadersgraph/mpsgraph/realtohermiteanfft(_:axestensor:descriptor:name:)"
---

# realToHermiteanFFT(_:axesTensor:descriptor:name:)

Creates a Real-to-Hermitean fast Fourier transform operation and returns the result tensor.

## Declaration

```swift
func realToHermiteanFFT(_ tensor: MPSGraphTensor, axesTensor: MPSGraphTensor, descriptor: MPSGraphFFTDescriptor, name: String?) -> MPSGraphTensor
```

## Parameters

- `tensor`: A real-valued input tensor. Must have datatype MPSDataTypeFloat32 , MPSDatatypeFloat16, or MPSDatatypeBFloat16.
- `axesTensor`: A tensor of rank one containing the axes over which MPSGraph performs the transformation. See doc://com.apple.metalperformanceshadersgraph/documentation/MetalPerformanceShadersGraph/MPSGraph/fastFourierTransform(_:axes:descriptor:name:).
- `descriptor`: A descriptor that defines the parameters of the Fourier transform operation - see doc://com.apple.metalperformanceshadersgraph/documentation/MetalPerformanceShadersGraph/MPSGraphFFTDescriptor.
- `name`: The name for the operation.

## Return Value

Return Value A valid MPSGraphTensor of type MPSDataTypeComplexFloat32, MPSDataTypeComplexFloat16 or MPSDataTypeComplexBFloat16 with reduced size (see Discussion).

## Discussion

Discussion This operation computes the fast Fourier transform of a real-valued input tensor according to the following formulae.     output[mu] = scale * sum_nu exp( +/- i * 2Pi * mu * nu / n ) input[nu], where scale = 1 for scaling_mode = none, scale = 1/V_f for scaling_mode = size, scale = 1/sqrt(V_f) for scaling_mode = unitary, where V_f is the volume of the transformation defined by the dimensions included in axes (V_f = prod_{i \in axes} shape(input)[i]) (see scalingMode), + is selected in +/- when inverse is specified, otherwise - is used and the sum is done separately over each dimension in axes and n is the dimension length of that axis. With this API MPSGraph writes out only the results for the unique frequencies, resulting in a tensor which has size (n/2)+1 in the last dimension defined by axes. tip: Currently MPSGraph supports the transformation only within the last four dimensions of the input tensor. In case you need to transform higher dimensions than the last four, you can tranpose the higher dimensions of the input with transpose(_:permutation:name:)  to be within that last four and then transpose the result tensor back with the inverse of the input transpose.