add optional stack remember on batch to AUdioMathNode

Author: mcDandy

more_math/AudioMathNode.py

@@ -48,6 +48,14 @@ def define_schema(cls) -> io.Schema:
                     tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
                 ),
                 io.Int.Input(id="batching", default=0),
+                io.Bool.Input(
+                    id="remember_stack",
+                    default=False,
+                    display_name="Remember stack across batch",
+                    tooltip=(
+                        "If enabled, stack is copied at output leading to changes being remembered during batch operations (node runs multiple times in sucession). If disabled each batch gets it's own copy of the stack."
+                    ),
+                ),
                 MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True)
             ],
             outputs=[
@@ -57,17 +65,17 @@ def define_schema(cls) -> io.Schema:
         )
 
     @classmethod
-    def check_lazy_status(cls, Expression, V, F, length_mismatch="tile",batching=0,stack={}):
+    def check_lazy_status(cls, Expression, V, F, length_mismatch="tile",batching=0,, remember_stack=False,stack={}):
         return checkLazyNew(Expression,V,F)
 
 
     @classmethod
-    def execute(cls, V, F, Expression, length_mismatch="tile",batching=0,stack={}):
+    def execute(cls, V, F, Expression, length_mismatch="tile",batching=0,, remember_stack=False, stack={}):
         # Identify all present audio inputs and their keys
         tensor_keys = [k for k, v in V.items() if v is not None and isinstance(v, dict) and "waveform" in v]
         if not tensor_keys:
              raise ValueError("At least one audio input is required.")
-        stack = copy.deepcopy(stack) if stack is not None else {}
+        stack = stack if remember_stack else (copy.deepcopy(stack) if stack is not None else {})
         waveforms = {k: V[k]["waveform"] for k in tensor_keys}
         sample_rates = {k + "sr": V[k].get("sample_rate", 44100) for k in tensor_keys}
 
@@ -142,4 +150,6 @@ def execute(cls, V, F, Expression, length_mismatch="tile",batching=0,stack={}):
                 res_list.append({"waveform": result_chunk, "sample_rate": sample_rate})
             return (res_list, stack)
         else:
+            stack = stack if remember_stack else copy.deepcopy(stack)
+
             return ([{"waveform": result, "sample_rate": sample_rate}], stack)