mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-08-06 14:10:13 -03:00
feat: add type-signature-based fallback for unregistered nodes
GenericNodeExtractor (previously a no-op) now inspects RETURN_TYPES to detect MODEL loaders and CONDITIONING encoders in nodes not registered in NODE_EXTRACTORS. - Propagate return_types from the hook layer through the registry to GenericNodeExtractor.extract() and update(). - MODEL detection: scan ckpt_name/unet_name/model_path/ model_name/gguf_name fields, validate by extension. - CONDITIONING detection: scan text/clip_l/t5xxl/prompt fields, store prompt text and conditioning tensor. - _fill_missing_metadata also checks node_cache, so GenericNodeExtractor-handled nodes survive cache.
This commit is contained in:
@@ -83,7 +83,8 @@ class MetadataHook:
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# Record inputs before execution
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if node_id is not None:
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registry.record_node_execution(node_id, class_type, input_data_all, None)
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return_types = getattr(obj.__class__, 'RETURN_TYPES', None)
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registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
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except Exception as e:
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logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
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@@ -114,7 +115,8 @@ class MetadataHook:
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# Record outputs after execution
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if node_id is not None:
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registry.update_node_execution(node_id, class_type, results)
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return_types = getattr(obj.__class__, 'RETURN_TYPES', None)
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registry.update_node_execution(node_id, class_type, results, return_types=return_types)
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except Exception as e:
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logger.error(f"Error collecting metadata (post-execution): {str(e)}")
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@@ -166,7 +168,8 @@ class MetadataHook:
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class_type = obj.__class__.__name__
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node_id = unique_id
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if node_id is not None:
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registry.record_node_execution(node_id, class_type, input_data_all, None)
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return_types = getattr(obj.__class__, 'RETURN_TYPES', None)
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registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
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except Exception as e:
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logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
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@@ -183,7 +186,8 @@ class MetadataHook:
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class_type = obj.__class__.__name__
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node_id = unique_id
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if node_id is not None:
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registry.update_node_execution(node_id, class_type, results)
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return_types = getattr(obj.__class__, 'RETURN_TYPES', None)
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registry.update_node_execution(node_id, class_type, results, return_types=return_types)
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except Exception as e:
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logger.error(f"Error collecting metadata (post-execution): {str(e)}")
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@@ -128,7 +128,7 @@ class MetadataRegistry:
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cache_key = f"{node_id}:{class_type}"
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# Check if this node type is relevant for metadata collection
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if class_type in NODE_EXTRACTORS:
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if class_type in NODE_EXTRACTORS or cache_key in self.node_cache:
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# Check if we have cached metadata for this node
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if cache_key in self.node_cache:
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cached_data = self.node_cache[cache_key]
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@@ -141,7 +141,7 @@ class MetadataRegistry:
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node_id
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]
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def record_node_execution(self, node_id, class_type, inputs, outputs):
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def record_node_execution(self, node_id, class_type, inputs, outputs, return_types=None):
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"""Record information about a node's execution"""
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if not self.current_prompt_id:
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return
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@@ -164,17 +164,18 @@ class MetadataRegistry:
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# Extract node-specific metadata
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extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
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extractor.extract(
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node_id,
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processed_inputs,
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outputs,
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self.prompt_metadata[self.current_prompt_id],
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)
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if type(extractor) is GenericNodeExtractor:
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extractor.extract(node_id, processed_inputs, outputs,
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self.prompt_metadata[self.current_prompt_id],
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return_types=return_types)
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else:
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extractor.extract(node_id, processed_inputs, outputs,
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self.prompt_metadata[self.current_prompt_id])
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# Cache this node's metadata
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self._cache_node_metadata(node_id, class_type)
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def update_node_execution(self, node_id, class_type, outputs):
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def update_node_execution(self, node_id, class_type, outputs, return_types=None):
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"""Update node metadata with output information"""
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if not self.current_prompt_id:
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return
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@@ -185,9 +186,17 @@ class MetadataRegistry:
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# Use the same extractor to update with outputs
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extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
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if hasattr(extractor, "update"):
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extractor.update(
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node_id, processed_outputs, self.prompt_metadata[self.current_prompt_id]
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)
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if type(extractor) is GenericNodeExtractor:
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extractor.update(
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node_id, processed_outputs,
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self.prompt_metadata[self.current_prompt_id],
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return_types=return_types,
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)
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else:
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extractor.update(
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node_id, processed_outputs,
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self.prompt_metadata[self.current_prompt_id],
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)
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# Update the cached metadata for this node
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self._cache_node_metadata(node_id, class_type)
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@@ -31,11 +31,78 @@ class NodeMetadataExtractor:
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pass
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class GenericNodeExtractor(NodeMetadataExtractor):
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"""Default extractor for nodes without specific handling"""
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"""Fallback extractor with type-signature-based detection.
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When a node is not in the NODE_EXTRACTORS registry, the hook layer
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passes ``return_types`` from ``obj.RETURN_TYPES``:
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* ``MODEL`` output: common input fields (ckpt_name, unet_name, etc.)
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are checked for a model file name and stored as checkpoint metadata.
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* ``CONDITIONING`` output: common text input fields are checked for
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prompt text and stored as prompt metadata.
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"""
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# Input field names that carry a model path in loader-style nodes.
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_MODEL_NAME_FIELDS = (
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"ckpt_name", "unet_name", "model_path", "model_name", "gguf_name",
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)
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# Extensions used by checkpoint_scanner.py — only record values that look
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# like real model filenames to avoid capturing unrelated string fields.
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_MODEL_EXTENSIONS = {
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".ckpt", ".pt", ".pt2", ".bin", ".pth", ".safetensors", ".pkl", ".sft", ".gguf",
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}
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# Input field names that may carry prompt text in encoder-style nodes.
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_TEXT_FIELDS = ("text", "clip_l", "t5xxl", "prompt", "positive", "negative")
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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pass
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def extract(node_id, inputs, outputs, metadata, return_types=None):
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if return_types is None:
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return
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# — MODEL loader detection (checkpoint / UNET / GGUF) —
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if "MODEL" in return_types or any("MODEL" in str(t) for t in return_types):
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for field in GenericNodeExtractor._MODEL_NAME_FIELDS:
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val = inputs.get(field)
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if val and isinstance(val, str) and val.strip():
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name = val.strip()
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if not any(name.lower().endswith(ext) for ext in GenericNodeExtractor._MODEL_EXTENSIONS):
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continue
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_store_checkpoint_metadata(metadata, node_id, name)
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return
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# — CONDITIONING encoder detection (CLIPTextEncode, Flux, custom) —
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if "CONDITIONING" in return_types or any("CONDITIONING" in str(t) for t in return_types):
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text = None
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for field in GenericNodeExtractor._TEXT_FIELDS:
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val = inputs.get(field)
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if val and isinstance(val, str) and val.strip():
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text = val.strip()
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break
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if text:
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prompt_data = metadata.setdefault(PROMPTS, {})
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prompt_data[node_id] = {
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"text": text,
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"node_id": node_id,
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}
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@staticmethod
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def update(node_id, outputs, metadata, return_types=None):
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if return_types is None:
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return
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if "CONDITIONING" not in return_types and not any(
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"CONDITIONING" in str(t) for t in return_types
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):
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return
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if node_id not in metadata.get(PROMPTS, {}):
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return
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if outputs and isinstance(outputs, list) and len(outputs) > 0:
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if isinstance(outputs[0], tuple) and len(outputs[0]) > 0:
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cond = outputs[0][0]
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if cond is not None:
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metadata[PROMPTS][node_id]["conditioning"] = cond
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class CheckpointLoaderExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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@@ -30,10 +30,10 @@ def test_metadata_hook_installs_and_traces_execution(monkeypatch, metadata_regis
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calls = []
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def record_stub(self, node_id, class_type, inputs, outputs):
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def record_stub(self, node_id, class_type, inputs, outputs, return_types=None):
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calls.append(("record", node_id, class_type, inputs))
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def update_stub(self, node_id, class_type, outputs):
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def update_stub(self, node_id, class_type, outputs, return_types=None):
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calls.append(("update", node_id, class_type, outputs))
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monkeypatch.setattr(MetadataRegistry, "record_node_execution", record_stub)
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