pub fn random_uniform_rng<'graph, A, F: Float, R: Rng + 'static>(
    arr_rng: ArrayRng<F, R>,
    shape: &A,
    min: f64,
    max: f64,
    graph: &'graph impl AsGraph<F>,
) -> Tensor<'graph, F>
where
    A: AsTensor<'graph, F>,
{
    let t = shape.as_tensor(graph);
    Tensor::builder(graph)
        .append_input(&t, false)
        .set_shape(&t)
        .build(random_ops::RandomUniform::new(arr_rng, min, max))
}
/// Outputs values sampled from the uniform distribution.
///
/// Pre-instantiated [ArrayRng](ndarray_ext/array_gen/struct.ArrayRng.html) is acceptable.
Outputs values sampled from the uniform distribution.
Outputs values sampled from the uniform distribution .
pub fn random_uniform_rng<'graph, A, F: Float, R: Rng + 'static>(
    arr_rng: ArrayRng<F, R>,
    shape: &A,
    min: f64,
    max: f64,
    graph: &'graph impl AsGraph<F>,
) -> Tensor<'graph, F>
where
    A: AsTensor<'graph, F>,
{
    let t = shape.as_tensor(graph);
    Tensor::builder(graph)
        .append_input(&t, false)
        .set_shape(&t)
        .build(random_ops::RandomUniform::new(arr_rng, min, max))
}
pub fn random_uniform_rng < ' graph , A , F : Float , R : Rng + ' static > ( arr_rng : ArrayRng < F , R > , shape : & A , min : f64 , max : f64 , graph : & ' graph impl AsGraph < F > , ) -> Tensor < ' graph , F > where A : AsTensor < ' graph , F > , { let t = shape . as_tensor ( graph ) ; Tensor :: builder ( graph ) . append_input ( & t , false ) . set_shape ( & t ) . build ( random_ops :: RandomUniform :: new ( arr_rng , min , max ) ) }
Outputs values sampled from the uniform distribution.
Outputs values sampled from the uniform distribution .
arr_rng
ArrayRng<F, R>
shape
&A
min
f64
max
f64
graph
&'graph impl AsGraph<F>
arr_rng
ArrayRng<F, R>
shape
&A
min
f64
max
f64
graph
&'graph impl AsGraph<F>