Add several new nodes:

- DecodeTranscriptNode to parse transcript text
- YoutubeUploadNode to upload videos to YouTube
- Audio metadata and file list nodes
- Image stack blending and stripping alpha channel
- Sampler dataset creation node
- Utils for deep copying tensors and arrays

Also includes:

- Testing for new utils module
- Git nodes marked with base class
- Renamed nodes.py to avoid conflicts
- Import refactoring to handle naming

Specifics:

- descript.py: New transcript parsing node
- youtube_nodes.py: New YouTube upload node
- audio.py: New audio metadata and file list nodes
- utils.py: Deep copy utility functions
- image.py: New image stack blending and alpha stripping
- tests/test_utils.py: Tests for new utils
- git.py: Marked git nodes with base class
- __init__.py: Refactored imports and mappings
- requirements.txt: Added YouTube dependencies
- functional.py: Minor tweaks
- modules/youtube.py: Helper for upload node
- video.py: Minor cleanup
- tests/test_youtube_nodes.py: Test for YouTube upload

tests/test_utils.py

@@ -0,0 +1,41 @@
+import torch
+import numpy as np
+from custom_nodes.EternalKernelLiteGraphNodes.utils import etk_deep_copy
+
+
+def test_etk_deep_copy():
+    original = {
+        "list": [1, 2, 3, [4, 5, 6], {"inner_list": [7, 8, 9]}],
+        "dict": {"inner_dict": {"key": "value"}}
+    }
+    torch_test = {"torch_tensor": torch.tensor([1, 2, 3])}
+    np_test = {"np_array": np.array([1, 2, 3])}
+
+    test(original)
+    test(torch_test)
+    test(np_test)
+
+
+def test(original):
+    # Creating a deep copy
+    copy = etk_deep_copy(original)
+
+    # Modifying the original structure
+    if "list" in original:
+        original["list"][0] = 100
+        if isinstance(original["list"][3], list):
+            original["list"][3][0] = 200
+        if "inner_list" in original["list"][4]:
+            original["list"][4]["inner_list"][0] = 300
+
+    if "dict" in original and "inner_dict" in original["dict"]:
+        original["dict"]["inner_dict"]["key"] = "modified"
+
+    if "torch_tensor" in original:
+        original["torch_tensor"][0] = 100
+
+    if "np_array" in original:
+        original["np_array"][0] = 200
+
+    # Returning both the original and the copy for inspection
+    return original, copy

tests/test_youtube_nodes.py

@@ -0,0 +1,17 @@
+import os
+
+os.environ["UNIT_TEST"] = "True"
+from custom_nodes.EternalKernelLiteGraphNodes.youtube_nodes import YoutubeUploadNode
+
+
+def test_upload():
+    node = YoutubeUploadNode()
+    output = node.upload_video(
+        file_path=os.path.join("tests", "example.mp4"),
+        category=22,
+        title="Test Video",
+        description="This is a test video for the Youtube Upload Node",
+        keywords="test, youtube, node",
+        privacy_status="unlisted"
+    )
+    print (output)