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[INFO] running `Command { std: "git" "-c" "credential.helper=" "-c" "credential.helper=/workspace/cargo-home/bin/git-credential-null" "clone" "--bare" "https://github.com/rmcfadden/classification-server-www" "/workspace/cache/git-repos/https%3A%2F%2Fgithub.com%2Frmcfadden%2Fclassification-server-www", kill_on_drop: false }`
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[INFO] running `Command { std: "git" "rev-parse" "HEAD", kill_on_drop: false }`
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[INFO] checking rmcfadden/classification-server-www against try#50f413885985a8b0a0b6ca0766a0aedd45304953 for pr-155657-1
[INFO] running `Command { std: "git" "clone" "/workspace/cache/git-repos/https%3A%2F%2Fgithub.com%2Frmcfadden%2Fclassification-server-www" "/workspace/builds/worker-3-tc2/source", kill_on_drop: false }`
[INFO] [stderr] Cloning into '/workspace/builds/worker-3-tc2/source'...
[INFO] [stderr] done.
[INFO] started tweaking git repo https://github.com/rmcfadden/classification-server-www
[INFO] finished tweaking git repo https://github.com/rmcfadden/classification-server-www
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[INFO] validating manifest of git repo https://github.com/rmcfadden/classification-server-www on toolchain 50f413885985a8b0a0b6ca0766a0aedd45304953
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[INFO] crate git repo https://github.com/rmcfadden/classification-server-www already has a lockfile, it will not be regenerated
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[INFO] [stdout] warning: unused variable: `i`
[INFO] [stdout]   --> src/core/input_vector.rs:38:18
[INFO] [stdout]    |
[INFO] [stdout] 38 |             for (i, input) in input_items.iter().enumerate() {
[INFO] [stdout]    |                  ^ help: if this is intentional, prefix it with an underscore: `_i`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `output_results`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:101:17
[INFO] [stdout]     |
[INFO] [stdout] 101 |             let output_results = output_layer.forward(&result_items)?;
[INFO] [stdout]     |                 ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_output_results`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `feature`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:111:29
[INFO] [stdout]     |
[INFO] [stdout] 111 |     async fn predict(&self, feature: L) -> Result<ClassifierResponse<L, V>, Box<dyn Error>> {
[INFO] [stdout]     |                             ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_feature`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `i`
[INFO] [stdout]   --> src/core/input_vector.rs:38:18
[INFO] [stdout]    |
[INFO] [stdout] 38 |             for (i, input) in input_items.iter().enumerate() {
[INFO] [stdout]    |                  ^ help: if this is intentional, prefix it with an underscore: `_i`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `output_results`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:101:17
[INFO] [stdout]     |
[INFO] [stdout] 101 |             let output_results = output_layer.forward(&result_items)?;
[INFO] [stdout]     |                 ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_output_results`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `feature`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:111:29
[INFO] [stdout]     |
[INFO] [stdout] 111 |     async fn predict(&self, feature: L) -> Result<ClassifierResponse<L, V>, Box<dyn Error>> {
[INFO] [stdout]     |                             ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_feature`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `normalizer`
[INFO] [stdout]   --> tests/math/binary_cross_entropy_loss_function_tests.rs:30:13
[INFO] [stdout]    |
[INFO] [stdout] 30 |         let normalizer = LossFunctionFactory::create::<f64>("binary_cross_entropy").unwrap();
[INFO] [stdout]    |             ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_normalizer`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `ouputs`
[INFO] [stdout]   --> tests/math/binary_cross_entropy_loss_function_tests.rs:32:13
[INFO] [stdout]    |
[INFO] [stdout] 32 |         let ouputs = vec![0.09003057317038046, 0.24472847105479767, 0.6652409557748219];
[INFO] [stdout]    |             ^^^^^^ help: if this is intentional, prefix it with an underscore: `_ouputs`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `targets`
[INFO] [stdout]   --> tests/math/binary_cross_entropy_loss_function_tests.rs:33:13
[INFO] [stdout]    |
[INFO] [stdout] 33 |         let targets = vec![0.24472847105479767, 0.09003057317038046, 0.6652409557748219];
[INFO] [stdout]    |             ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_targets`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `i`
[INFO] [stdout]   --> src/core/input_vector.rs:38:18
[INFO] [stdout]    |
[INFO] [stdout] 38 |             for (i, input) in input_items.iter().enumerate() {
[INFO] [stdout]    |                  ^ help: if this is intentional, prefix it with an underscore: `_i`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `output_results`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:101:17
[INFO] [stdout]     |
[INFO] [stdout] 101 |             let output_results = output_layer.forward(&result_items)?;
[INFO] [stdout]     |                 ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_output_results`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `feature`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:111:29
[INFO] [stdout]     |
[INFO] [stdout] 111 |     async fn predict(&self, feature: L) -> Result<ClassifierResponse<L, V>, Box<dyn Error>> {
[INFO] [stdout]     |                             ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_feature`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `i`
[INFO] [stdout]   --> src/core/input_vector.rs:38:18
[INFO] [stdout]    |
[INFO] [stdout] 38 |             for (i, input) in input_items.iter().enumerate() {
[INFO] [stdout]    |                  ^ help: if this is intentional, prefix it with an underscore: `_i`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `output_results`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:101:17
[INFO] [stdout]     |
[INFO] [stdout] 101 |             let output_results = output_layer.forward(&result_items)?;
[INFO] [stdout]     |                 ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_output_results`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `feature`
[INFO] [stdout]    --> src/models/perceptron_neural_model.rs:111:29
[INFO] [stdout]     |
[INFO] [stdout] 111 |     async fn predict(&self, feature: L) -> Result<ClassifierResponse<L, V>, Box<dyn Error>> {
[INFO] [stdout]     |                             ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_feature`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished `dev` profile [unoptimized + debuginfo] target(s) in 1m 29s
[INFO] running `Command { std: "docker" "inspect" "d4b140d856a7c502f764f5e788ec50aad5bfeea0f7cdb2d2bab2f9255d49656c", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "d4b140d856a7c502f764f5e788ec50aad5bfeea0f7cdb2d2bab2f9255d49656c", kill_on_drop: false }`
[INFO] [stdout] d4b140d856a7c502f764f5e788ec50aad5bfeea0f7cdb2d2bab2f9255d49656c
