[NPM] Fix prebuilt conntracker warning

[hmahmood]

What does this PR do?

Fix a warning log for when we load the deprecated prebuilt conntracker. This log was getting emitted in cases where we would not load the prebuilt version of the conntracker.

Motivation

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=48279902 --os-family=ubuntu

Note: This applies to commit b7aad73

[hmahmood]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 22m.

Use /merge -c to cancel this operation!

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: cc0e2efd-735c-4dc2-8ab3-f77e52a7acfa

Baseline: 88a940f
Comparison: b7aad73
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.72	[-0.01, +1.45]	1	Logs
➖	file_tree	memory utilization	+0.54	[+0.41, +0.67]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.31	[+0.26, +0.36]	1	Logs
➖	idle	memory utilization	+0.16	[+0.11, +0.21]	1	Logs bounds checks dashboard
➖	file_to_blackhole_300ms_latency	egress throughput	+0.02	[-0.16, +0.20]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.32, +0.34]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.10, +0.13]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.03	[-0.28, +0.22]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.04	[-0.26, +0.19]	1	Logs
➖	idle_all_features	memory utilization	-0.25	[-0.35, -0.14]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.39	[-0.88, +0.11]	1	Logs
➖	quality_gate_idle	memory utilization	-0.54	[-0.59, -0.49]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	-0.69	[-0.79, -0.59]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	-0.84	[-1.64, -0.03]	1	Logs
➖	basic_py_check	% cpu utilization	-0.92	[-4.69, +2.85]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-2.75	[-6.19, +0.69]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10	bounds checks dashboard
✅	idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".