fix: resolve basicsr/torchvision shim bug, lint warnings, and code formatting

The torchvision compatibility shim for basicsr 1.4.2 was missing the
parent-package binding and only proxied a single attribute, causing
upscale and enhance-faces to fail at import time. The fix adds a
__getattr__ proxy for all attributes, binds the shim to the parent
package, and installs it in the dispatcher at startup for defense-in-depth.

Also removes unused anyInstalling variable, redundant `as any` cast,
and applies Biome formatting fixes across the codebase.

packages/ai/python/dispatcher.py

@@ -49,6 +49,36 @@ def emit_progress(percent, stage):
     print(json.dumps({"progress": percent, "stage": stage}), file=sys.stderr, flush=True)
 
 
+# ── basicsr / torchvision compatibility shim ──────────────────────────
+# basicsr 1.4.2 (pulled in by realesrgan) does:
+#   from torchvision.transforms.functional_tensor import rgb_to_grayscale
+# but torchvision >= 0.17 removed the functional_tensor submodule,
+# merging everything into torchvision.transforms.functional.
+# We install a shim module ONCE here so every script in this process
+# benefits, rather than relying on each script to patch individually.
+try:
+    import torchvision.transforms.functional_tensor  # noqa: F401
+except (ImportError, ModuleNotFoundError):
+    try:
+        import types
+        import torchvision.transforms.functional as _F
+        import torchvision.transforms
+
+        _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        _shim.__getattr__ = lambda name: getattr(_F, name)
+        _shim.rgb_to_grayscale = _F.rgb_to_grayscale
+        sys.modules["torchvision.transforms.functional_tensor"] = _shim
+        torchvision.transforms.functional_tensor = _shim
+        print("[dispatcher] Installed torchvision.transforms.functional_tensor shim",
+              file=sys.stderr, flush=True)
+    except (ImportError, AttributeError):
+        # torchvision not installed yet — shim not needed until
+        # the upscale-enhance bundle is installed.
+        pass
+except Exception:
+    # Catch-all so dispatcher startup is never blocked.
+    pass
+
 # ── Pre-import heavy libraries ──────────────────────────────────────
 # These imports are the main source of cold-start latency.
 # By importing once at startup, subsequent requests skip the import cost.

packages/ai/python/enhance_faces.py

@@ -3,10 +3,11 @@ import sys
 import json
 import os
 
-# Patch for basicsr compatibility with torchvision >= 0.18.
-# torchvision removed transforms.functional_tensor, merging it into
-# transforms.functional. basicsr still imports the old path, so we
-# create a shim module to redirect the import.
+# Patch for basicsr compatibility with torchvision >= 0.17.
+# torchvision removed transforms.functional_tensor, merging everything
+# into transforms.functional. basicsr 1.4.2 still imports the old path
+# (e.g. rgb_to_grayscale), so we create a proxy module that forwards
+# ALL attribute lookups to the new location.
 try:
     import torchvision.transforms.functional_tensor  # noqa: F401
 except (ImportError, ModuleNotFoundError):
@@ -14,10 +15,20 @@ except (ImportError, ModuleNotFoundError):
         import types
         import torchvision.transforms.functional as _F
 
+        import torchvision.transforms
+
         _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        _shim.__getattr__ = lambda name: getattr(_F, name)
+        # Pre-populate the attribute basicsr actually imports so that
+        # `from torchvision.transforms.functional_tensor import rgb_to_grayscale`
+        # works (from-import checks __dict__ before __getattr__).
         _shim.rgb_to_grayscale = _F.rgb_to_grayscale
         sys.modules["torchvision.transforms.functional_tensor"] = _shim
-    except ImportError as e:
+        # The parent package must also reference the submodule for
+        # `from torchvision.transforms.functional_tensor import ...` to
+        # resolve correctly in all Python versions.
+        torchvision.transforms.functional_tensor = _shim
+    except (ImportError, AttributeError) as e:
         print(f"[enhance-faces] torchvision shim failed: {e}", file=sys.stderr, flush=True)
 
 

packages/ai/python/upscale.py

@@ -3,10 +3,11 @@ import sys
 import json
 import os
 
-# Patch for basicsr compatibility with torchvision >= 0.18.
-# torchvision removed transforms.functional_tensor, merging it into
-# transforms.functional. basicsr still imports the old path, so we
-# create a shim module to redirect the import.
+# Patch for basicsr compatibility with torchvision >= 0.17.
+# torchvision removed transforms.functional_tensor, merging everything
+# into transforms.functional. basicsr 1.4.2 still imports the old path
+# (e.g. rgb_to_grayscale), so we create a proxy module that forwards
+# ALL attribute lookups to the new location.
 try:
     import torchvision.transforms.functional_tensor  # noqa: F401
 except (ImportError, ModuleNotFoundError):
@@ -14,10 +15,20 @@ except (ImportError, ModuleNotFoundError):
         import types
         import torchvision.transforms.functional as _F
 
+        import torchvision.transforms
+
         _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        _shim.__getattr__ = lambda name: getattr(_F, name)
+        # Pre-populate the attribute basicsr actually imports so that
+        # `from torchvision.transforms.functional_tensor import rgb_to_grayscale`
+        # works (from-import checks __dict__ before __getattr__).
         _shim.rgb_to_grayscale = _F.rgb_to_grayscale
         sys.modules["torchvision.transforms.functional_tensor"] = _shim
-    except ImportError as e:
+        # The parent package must also reference the submodule for
+        # `from torchvision.transforms.functional_tensor import ...` to
+        # resolve correctly in all Python versions.
+        torchvision.transforms.functional_tensor = _shim
+    except (ImportError, AttributeError) as e:
         print(f"[upscale] torchvision shim failed: {e}", file=sys.stderr, flush=True)