remove todo in toml.cargo and fmt & lints.

Cargo.toml

@@ -226,7 +226,7 @@ sha2 = "0.10.8"
 sha3 = "0.10.8"
 slugify-rs = "0.0.3"
 smallvec = { version = "1.11.1", features = ["serde"] }
-sorted-insert = "0.2.3"# TODO "0.16.0"
+sorted-insert = "0.2.3"
 subtle = { version = "2.5.0", default-features = false }
 tempfile = "3.8.1"
 textwrap = "0.16.0"

components/addressmanager/src/lib.rs

@@ -519,7 +519,7 @@ mod address_store_with_cache {
         use kaspa_core::task::tick::TickService;
         use kaspa_database::create_temp_db;
         use kaspa_database::prelude::ConnBuilder;
-        use kaspa_utils::{as_slice::AsSlice, networking::IpAddress};
+        use kaspa_utils::networking::IpAddress;
         use kolmogorov_smirnov::test::test as two_way_ks_test;
         use std::net::{IpAddr, Ipv6Addr};
 
@@ -593,7 +593,7 @@ mod address_store_with_cache {
             let num_of_trials = 512;
             let mut cul_p = 0.;
             // The target uniform distribution
-            let target_u_dist = Vec::from_iter((0..num_of_buckets));
+            let target_u_dist = Vec::from_iter(0..num_of_buckets);
             for _ in 0..num_of_trials {
                 // The weight sampled expected uniform distibution
                 let prioritized_address_distribution = am
@@ -602,16 +602,17 @@ mod address_store_with_cache {
                     .take(num_of_buckets)
                     .map(|addr| addr.prefix_bucket().as_u64() as usize)
                     .collect_vec();
-                cul_p += (two_way_ks_test(
-                    prioritized_address_distribution.as_slice(), 
+                cul_p += two_way_ks_test(
+                    prioritized_address_distribution.as_slice(),
                     target_u_dist.as_slice(),
-                    0.5 // confidence is unimportant here, as we are just culminating reject_probabilities for now.  
-                ).reject_probability 
-                / 2.0); // Divide by 2 as we want to test one way. 
+                    0.5, // confidence is unimportant here, as we are just culminating reject_probabilities for now.
+                )
+                .reject_probability
+                    / 2.0; // Divide by 2 as we want to test one way.
             }
 
             // Normalize and adjust p to test for uniformity, over average of all trials.
-            let adjusted_p = cul_p / num_of_trials as f64; 
+            let adjusted_p = cul_p / num_of_trials as f64;
             // Define the significance threshold.
             let significance = 0.10;