Resolvers

This page describes some of the resolvers that are currently implemented.

Default

hya.add

This resolver adds the two inputs. The following config

value: ${hya.add:object1,object2}

is equivalent to:

value = object1 + object2

It is possible to add more than 2 inputs. The following config

value: ${hya.add:object1,object2,object3,object4}

is equivalent to:

value = object1 + object2 + object3 + object4

hya.floordiv

This resolver computes the "true" division between two inputs. The following config

value: ${hya.floordiv:dividend,divisor}

is equivalent to:

value = dividend // divisor

hya.neg

This resolver computes the negation of the input. The following config

value: ${hya.neg:number}

is equivalent to:

value = -number

hya.mul

This resolver multiplies the two inputs. The following config

value: ${hya.mul:object1,object2}

is equivalent to:

value = object1 * object2

It is possible to multiply more than 2 inputs. The following config

value: ${hya.mul:object1,object2,object3,object4}

is equivalent to:

value = object1 * object2 * object3 * object4

hya.pow

This resolver computes a value to a given power. The following config

value: ${hya.pow:fraction,exponent}

is equivalent to:

value = fraction**exponent

hya.sqrt

This resolver computes a squared root value of a number. The following config

value: ${hya.sqrt:number}

is equivalent to:

import math

value = math.sqrt(number)

hya.sha256

This resolver computes the SHA-256 hash of an object.

hya.sub

This resolver subtracts the two inputs. The following config

value: ${hya.sub:object1,object2}

is equivalent to:

value = object1 - object2

hya.to_path

This resolver transforms the input string to a pathlib.Path.

hya.truediv

This resolver computes the "true" division between two inputs. The following config

value: ${hya.truediv:dividend,divisor}

is equivalent to:

value = dividend / divisor

PyTorch

You need to install PyTorch to use these resolvers.

hya.torch.tensor

This resolver transforms the input to a torch.Tensor. The following config

value: ${hya.torch.tensor:[1,2,3]}

is equivalent to:

import torch

value = torch.tensor([1, 2, 3])