feat: Metrics Support in tritonfrontend

[KrishnanPrash]

What does the PR do?

Adding support for Metrics in tritonfrontend. This involves two components:

• In tritonfrontend_pybind.cc, added bindings for HTTPMetricsServer
• In tritonfrontend/_api/_metrics.py, added a Metrics class

With this PR, similar to KServeHttp and KServeGrpc, the metrics service can used with:

import tritonserver
from tritonfrontend import Metrics
server = tritonserver.Server(model_repostory=...).start(wait_until_ready=True)
metrics_service = Metrics(server)
metrics_service.start()
...
metrics_service.stop()

Additional Changes made in this PR:

• Modified test functions documentation based on this comment
• Removed extra parameter in request.post(...) based on this comment

Test plan:

Added 3 test function to L0_python_api:

• test_metrics_default_port() : Tests whether the metrics service can start as expected
• test_metrics_custom_port(): Tests whether arguments defined in tritonfrontend.Metrics.Options are passed successfully to HTTPMetrics
• test_metrics_update(): Tests whether nv_inference_count value goes from 0 to 1 if inference request is performed.
• CI Pipeline ID: 19197748

[rmccorm4]

I don't love that the Metrics object is a web server, so it makes me wonder if we should rename these down the line, ex: KServeHttpService, MetricsService, etc

But I don't have a strong opinion on an alternative right now so I think it's fine, just mentioning for later. We will probably be restructing some packaging and naming in the near-mid future.

[rmccorm4]

Only minor comments - nice work!