Update test results directory structure and enhance Docker configurations

- Modified `.gitignore` to reflect the new path for test results.
- Updated `docker-compose.test.yml` to mount the correct test results directory.
- Adjusted `Dockerfile.test` to set the `PYTHONPATH` and ensure test results are saved in the updated location.
- Added `boto3` and `netron` to `requirements-test.txt` to support new functionalities.
- Updated `pytest.ini` to include the new `pythonpath` for test discovery.

These changes streamline the testing process and ensure compatibility with the updated directory structure.

tests/performance/test_dataset_perf.py

@@ -1,7 +1,5 @@
 import shutil
-import sys
 import time
-import types
 from os import path as osp
 from pathlib import Path
 
@@ -10,40 +8,6 @@ import pytest
 import constants as c_mod
 
 
-def _stub_train_dependencies():
-    if getattr(_stub_train_dependencies, "_done", False):
-        return
-
-    def add_mod(name):
-        if name in sys.modules:
-            return sys.modules[name]
-        m = types.ModuleType(name)
-        sys.modules[name] = m
-        return m
-
-    ultra = add_mod("ultralytics")
-
-    class YOLO:
-        pass
-
-    ultra.YOLO = YOLO
-
-    def fake_client(*_a, **_k):
-        return types.SimpleNamespace(
-            upload_fileobj=lambda *_a, **_k: None,
-            download_file=lambda *_a, **_k: None,
-        )
-
-    boto = add_mod("boto3")
-    boto.client = fake_client
-    add_mod("netron")
-    add_mod("requests")
-    _stub_train_dependencies._done = True
-
-
-_stub_train_dependencies()
-
-
 def _prepare_form_dataset(
     monkeypatch,
     tmp_path,
@@ -82,6 +46,7 @@ def test_pt_dsf_01_dataset_formation_under_thirty_seconds(
     fixture_images_dir,
     fixture_labels_dir,
 ):
+    # Arrange
     train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
@@ -91,7 +56,9 @@ def test_pt_dsf_01_dataset_formation_under_thirty_seconds(
         100,
         set(),
     )
+    # Act
     t0 = time.perf_counter()
     train.form_dataset()
     elapsed = time.perf_counter() - t0
+    # Assert
     assert elapsed <= 30.0