Is there an example of range related operation

[Jzow]

this is my code

fn tf_ops_range() {
    // Create a range op that produces [0, 1, 2, 3, 4, 5]
    let mut scope = tensorflow::Scope::new_root_scope();
    let start = tensorflow::ops::constant(0_i32, &mut scope);
    let limit = tensorflow::ops::constant(6_i32, &mut scope);
    let delta = tensorflow::ops::constant(1_i32, &mut scope);

    let range_op = tensorflow::ops::range(start, limit, delta, &mut scope).unwrap();
}

// test tf_ops_range
#[test]
fn test_tf_ops_range() {
    tf_ops_range();
}

report error:

let range_op = tensorflow::ops::range(start, limit, delta, &mut scope).unwrap();
       |                    ---------------------- ^^^^^ the trait `From<Result<Operation, Status>>` is not implemented for `tensorflow::Output`
       |                    |
       |                    required by a bound introduced by this call
       |
       = help: the trait `From<Operation>` is implemented for `tensorflow::Output`
       = note: required for `Result<Operation, Status>` to implement `Into<tensorflow::Output>`

I looked through the relevant documents, including the source code, but unfortunately I didn't see an example of how to use range

Shorthand for Range::new().build(start, limit, delta, scope).

[Jzow]

When I changed to the following code, he output the results as I expected. range -> constant

use tensorflow::{Scope as scope, Tensor};

fn tf_ops_range() {

    let mut scope = scope::new_root_scope();
    let x = tensorflow::ops::constant(&[0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11][..], &mut scope).unwrap();
    println!("x type: {:?}", x.get_attr_type("dtype").unwrap());

    let value: Tensor<i32> = x.get_attr_tensor("value").unwrap();
    println!("x value: {:?}", value);
}

// test tf_ops_range
#[test]
fn test_tf_ops_range() {
    tf_ops_range();
}

I want to create a line vector x. The row vector contains the first 12 integers starting at 0, which are created by default as integers, for python example

import tensorflow as tf

x = tf.range(12)
println(x)

<tf.Tensor: shape=(12,), dtype=int32, numpy=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], dtype=int32)>