fix: scope the torch.load map_location wrapper instead of replacing it globally

`chatterbox_node.py` did `torch.load = patched_torch_load` at import time,
which replaces torch.load for the entire Python process — ComfyUI core and
every other custom node pack included.

Two problems in a shared environment (and on multi-tenant cloud runtimes
where one process serves many users' jobs back to back):

1. Cross-pack clobbering. Other packs also wrap `torch.load`. Whichever
   imports last wins, so process-wide torch.load behavior depends on
   custom-node import order, which is not deterministic.
2. Behavior imposed on unrelated callers. After import, every `torch.load`
   in the process gets `map_location` forced onto it — including ComfyUI
   core's checkpoint loading and other packs that explicitly wanted default
   device placement.

Fix: keep `patched_torch_load` exactly as-is, but install it only for the
duration of this pack's own Chatterbox model loads via a
`default_map_location()` context manager, restoring the previous torch.load
in `finally`. All four `Chatterbox*.from_local(...)` call sites are wrapped,
so device defaulting still works for every Chatterbox load; torch.load is
left untouched for everyone else.

No functional change to how this pack loads models; only the blast radius of
the patch is reduced from process-global to call-scoped.

chatterbox_node.py

@@ -1,4 +1,5 @@
 import os
+import contextlib
 import torch
 import torchaudio
 import numpy as np
@@ -156,7 +157,8 @@ def load_turbo_model(device: str) -> ChatterboxTurboTTS:
     ]
 
     download_chatterbox_models("ResembleAI/chatterbox-turbo", turbo_files, local_dir)
-    return ChatterboxTurboTTS.from_local(str(local_dir), device)
+    with default_map_location():
+        return ChatterboxTurboTTS.from_local(str(local_dir), device)
 
 
 def load_tts_model(device: str) -> ChatterboxTTS:
@@ -177,7 +179,8 @@ def load_tts_model(device: str) -> ChatterboxTTS:
     ]
 
     download_chatterbox_models("ResembleAI/chatterbox", tts_files, local_dir)
-    return ChatterboxTTS.from_local(str(local_dir), device)
+    with default_map_location():
+        return ChatterboxTTS.from_local(str(local_dir), device)
 
 
 def load_multilingual_model(device: str) -> ChatterboxMultilingualTTS:
@@ -199,7 +202,8 @@ def load_multilingual_model(device: str) -> ChatterboxMultilingualTTS:
     ]
 
     download_chatterbox_models("ResembleAI/chatterbox", mtl_files, local_dir)
-    return ChatterboxMultilingualTTS.from_local(str(local_dir), device)
+    with default_map_location():
+        return ChatterboxMultilingualTTS.from_local(str(local_dir), device)
 
 
 def load_vc_model(device: str) -> ChatterboxVC:
@@ -218,9 +222,16 @@ def load_vc_model(device: str) -> ChatterboxVC:
     ]
 
     download_chatterbox_models("ResembleAI/chatterbox", vc_files, local_dir)
-    return ChatterboxVC.from_local(str(local_dir), device)
+    with default_map_location():
+        return ChatterboxVC.from_local(str(local_dir), device)
 
-# Monkey patch torch.load to use MPS or CPU if map_location is not specified
+# torch.load wrapper: default map_location to the active device (MPS / CUDA /
+# CPU) when the caller did not specify one. Installed via a context manager
+# rather than replacing torch.load process-wide — a global replacement
+# clobbers (and is clobbered by) other custom node packs that also wrap
+# torch.load, and forces map_location onto ComfyUI core and every other pack
+# that never opted in. Scoping keeps the device-defaulting only for this
+# pack's own Chatterbox model loads.
 original_torch_load = torch.load
 def patched_torch_load(*args, **kwargs):
     if 'map_location' not in kwargs:
@@ -234,7 +245,16 @@ def patched_torch_load(*args, **kwargs):
         kwargs['map_location'] = torch.device(device)
     return original_torch_load(*args, **kwargs)
 
-torch.load = patched_torch_load
+
+@contextlib.contextmanager
+def default_map_location():
+    """Temporarily install patched_torch_load for this pack's model loads."""
+    previous = torch.load
+    torch.load = patched_torch_load
+    try:
+        yield
+    finally:
+        torch.load = previous
 
 
 class AudioNodeBase: