tensor.min

   fn min(tensors: Span<Tensor<T>>) -> Tensor<T>;

Returns the element-wise minimum values from a list of input tensors The input tensors must have either:

• Exactly the same shape

• The same number of dimensions and the length of each dimension is either a common length or 1.

Args

• tensors( Span<Tensor<T>>,) - Array of the input tensors

Returns

A new Tensor<T> containing the element-wise minimum values

Panics

• Panics if tensor array is empty

• Panics if the shapes are not equal or broadcastable

Examples

Case 1: Process tensors with same shape

use core::array::{ArrayTrait, SpanTrait};

use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};

fn min_example() -> Tensor<u32> {
    let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),);
    let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 3, 1, 2].span(),);
    let result = TensorTrait::min(tensors: array![tensor1, tensor2].span());
    return result;
}
>>> [0, 1, 1, 2]

    result.shape
>>> (2, 2)

Case 2: Process tensors with different shapes

use core::array::{ArrayTrait, SpanTrait};

use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};

fn min_example() -> Tensor<u32> {
    let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),);
    let tensor2 = TensorTrait::new(shape: array![1, 2].span(), data: array![1, 4].span(),);
    let result = TensorTrait::min(tensors: array![tensor1, tensor2].span());
    return result;
}
>>> [0, 1, 1, 4]

    result.shape
>>> (2, 2)