NaN for [N, C, 1, W] interpolate inputs with bilinear and bicubic modes for burn-wgpu and burn-ndarray backends

[antimora]

Describe the bug
NaN values when the input tensor is [N, C, 1, W] shape.

To Reproduce

1. Enable test_1d_bilinear test in crates/burn-tensor/src/tests/module/bilinear_interpolate.rs and test_1d_bicubic in crates/burn-tensor/src/tests/module/bilinear_interpolate.rs which are ignored for now. (Copy of tests are in the comments)
2. cd burn-wgpu && cargo test
3. cd burn-ndarray && cargo test
4. Enable resize_with_scales_1d_linear test in crates/burn-import/onnx-tests/tests/onnx_tests.rs which is ignored.

NOTE:

These tests work under burn-tch and pytorch.

Related Issues:

1. Interpolate tensor operation (Inference Only)  #1246
2. Add 1d and 2d modules for interpolate with scaling (also fix ONNX Resize op) #2081

[antimora]

Ignored tests just in case the code gets deleted:

    #[test]
    #[ignore = "https://github.com/tracel-ai/burn/issues/2080"]
    fn test_1d_bicubic() {
        // Initialize the model without weights (because the exported file does not contain them)
        let device = Default::default();

        // Run the model
        let input = TestTensor::<3>::from_floats(
            [[[1.5410, -0.2934, -2.1788, 0.5684, -1.0845, -1.3986]]],
            &device,
        );

        let input = input.unsqueeze_dim(2);

        let output = interpolate(
            input,
            [1, 9],
            InterpolateOptions::new(InterpolateMode::Bicubic),
        );

        assert_eq!(output.dims(), [1, 1, 1, 9]);

        // assert output data does not contain NaN
        assert!(
            !output
                .clone()
                .to_data()
                .as_slice::<f32>()
                .unwrap()
                .iter()
                .any(|&x| x.is_nan()),
            "interpolate output contains NaN"
        );

        TestTensor::<4>::from([[[[
            1.541, 0.5747652, -1.010614, -2.197787, -0.8269969, 0.59609234, -0.5803058, -1.3792794,
            -1.3986,
        ]]]])
        .to_data()
        .assert_approx_eq(&output.into_data(), 3);
    }


    #[test]
    #[ignore = "https://github.com/tracel-ai/burn/issues/2080"]
    fn test_1d_bilinear() {
        // Initialize the model without weights (because the exported file does not contain them)
        let device = Default::default();

        // Run the model
        let input = TestTensor::<3>::from_floats(
            [[[1.5410, -0.2934, -2.1788, 0.5684, -1.0845, -1.3986]]],
            &device,
        );

        let input = input.unsqueeze_dim(2);

        let output = interpolate(
            input,
            [1, 9],
            InterpolateOptions::new(InterpolateMode::Bilinear),
        );

        assert_eq!(output.dims(), [1, 1, 1, 9]);

        // assert output data does not contain NaN
        assert!(
            !output
                .clone()
                .to_data()
                .as_slice::<f32>()
                .unwrap()
                .iter()
                .any(|&x| x.is_nan()),
            "interpolate output contains NaN"
        );

        TestTensor::<4>::from([[[[
            1.541f32,
            0.39450002,
            -0.76475,
            -1.943125,
            -0.80520004,
            0.36178753,
            -0.671275,
            -1.2022874,
            -1.3986,
        ]]]])
        .to_data()
        .assert_approx_eq(&output.into_data(), 3);
    }

[antimora]

CC @laggui @louisfd