diff --git a/source/pytorch.js b/source/pytorch.js
index 825c1cfebe..1b0f097de4 100644
--- a/source/pytorch.js
+++ b/source/pytorch.js
@@ -107,16 +107,13 @@ pytorch.Graph = class {
             const queue = [module.data];
             while (queue.length > 0) {
                 const module = queue.shift();
-                if (pytorch.Utility.isInstance(module, '__torch__.torch.classes._nnapi.Compilation')) {
-                    continue;
-                }
                 for (const [key, obj] of Object.entries(module)) {
                     if (key !== '__module__' && key !== '__name__' && key !== '__class__' && key !== '__parent__') {
                         if (!Array.isArray(obj) && obj === Object(obj)) {
                             if (pytorch.Utility.isTensor(obj)) {
                                 const parameter = obj;
                                 parameter.__parent__ = module;
-                                if (parameter.storage()) {
+                                if (parameter.storage() && !parameter.__origin__) {
                                     if (parameter.__count__ === undefined || parameter.__count__ === 1) {
                                         initializers.set(parameter, new pytorch.Tensor(parameter.name, parameter));
                                     }
@@ -161,7 +158,7 @@ pytorch.Graph = class {
                     node.outputs().every((output) => pytorch.Utility.isTensor(output.value))) {
                     continue;
                 }
-                this.nodes.push(new pytorch.Node(metadata, node, initializers, values));
+                this.nodes.push(new pytorch.Node(metadata, null, null, node, initializers, values));
             }
             if (module) {
                 const queue = [module.data];
@@ -169,8 +166,12 @@ pytorch.Graph = class {
                     const module = queue.pop();
                     if (module && !pytorch.Utility.isObject(module)) {
                         if (!module.__hide__ && pytorch.Graph._getParameters(module).size > 0) {
-                            const item = { module };
-                            const node = new pytorch.Node(metadata, item, initializers, values);
+                            for (const [name, obj] of Object.entries(module)) {
+                                if ((obj && obj.__hide__) || (obj !== null && !pytorch.Utility.isTensor(obj)) && typeof obj !== 'boolean' && typeof obj !== 'number' && typeof obj !== 'string') {
+                                    delete module[name];
+                                }
+                            }
+                            const node = new pytorch.Node(metadata, null, null, module, initializers, values);
                             this.nodes.push(node);
                         }
                         const modules = [];
@@ -186,32 +187,20 @@ pytorch.Graph = class {
                 }
             }
         } else if (pytorch.Utility.isTensor(module)) {
-            const item = { type, obj: { value: module } };
-            const node = new pytorch.Node(metadata, item);
+            const node = new pytorch.Node(metadata, null, type, { value: module });
             this.nodes.push(node);
         } else {
             const weights = this.type === 'weights' ? module : pytorch.Utility.weights(module);
             if (weights) {
                 for (const [name, module] of weights) {
-                    const item = { name, type: 'Weights', obj: module };
-                    const node = new pytorch.Node(metadata, item);
+                    const node = new pytorch.Node(metadata, name, 'Weights', module);
                     this.nodes.push(node);
                 }
             } else {
                 const modules = Array.isArray(module) && module.every((module) => module && !pytorch.Utility.isTensor(module) && (module._modules !== undefined || module.__class__)) ? module : [module];
                 for (const module of modules) {
-                    let type = module.__class__ && module.__class__.__module__ && module.__class__.__name__ ? `${module.__class__.__module__}.${module.__class__.__name__}` : null;
-                    if (type === 'torch.jit._script.RecursiveScriptModule' && module._c && module._c.qualified_name) {
-                        type = module._c.qualified_name;
-                    }
-                    if (!type) {
-                        type = this.type === 'weights' ? 'Weights' : 'builtins.dict';
-                    }
-                    const item = {
-                        type,
-                        obj: module
-                    };
-                    const node = new pytorch.Node(metadata, item, {}, values);
+                    const type = this.type === 'weights' ? 'Weights' : null;
+                    const node = new pytorch.Node(metadata, null, type, module, null, values);
                     this.nodes.push(node);
                 }
             }
@@ -260,13 +249,13 @@ pytorch.Value = class {
 
 pytorch.Node = class {
 
-    constructor(metadata, item, initializers, values, stack) {
-        this.name = item.name || '';
+    constructor(metadata, name, type, obj, initializers, values, stack) {
+        this.name = name || '';
         this.nodes = [];
         this.attributes = [];
         this.inputs = [];
         this.outputs = [];
-        const type = (metadata, name) => {
+        const createType = (metadata, name) => {
             if (name instanceof pytorch.nnapi.Graph) {
                 return name;
             }
@@ -305,9 +294,9 @@ pytorch.Node = class {
             return new pytorch.Argument(name, value, type, visible);
         };
         let module = null;
-        if (pytorch.Utility.isInstance(item, 'torch.Node')) {
-            const node = item;
-            this.type = type(metadata, node.kind());
+        if (pytorch.Utility.isInstance(obj, 'torch.Node')) {
+            const node = obj;
+            this.type = createType(metadata, node.kind());
             let match = true;
             let count = 0;
             for (const input of node.inputs()) {
@@ -366,10 +355,10 @@ pytorch.Node = class {
                 if (pytorch.Utility.isObjectType(type)) {
                     const obj = input.value;
                     if (!array && initializers.has(obj)) {
-                        const node = new pytorch.Node(metadata, { name, type, obj }, initializers, values);
+                        const node = new pytorch.Node(metadata, name, type, obj, initializers, values);
                         argument = new pytorch.Argument(name, node, 'object');
                     } else if (array && Array.isArray(obj) && obj.every((obj) => initializers.has(obj))) {
-                        const node = obj.map((obj) => new pytorch.Node(metadata, { name, type, obj }, initializers, values));
+                        const node = obj.map((obj) => new pytorch.Node(metadata, name, type, obj, initializers, values));
                         argument = new pytorch.Argument(name, node, 'object[]');
                     } else {
                         const identifier = input.unique().toString();
@@ -425,30 +414,20 @@ pytorch.Node = class {
                 const argument = new pytorch.Argument(name, args);
                 this.outputs.push(argument);
             }
-        } else if (item.module) {
-            module = item.module;
-            const type = module.__class__ ? `${module.__class__.__module__}.${module.__class__.__name__}` : 'torch.nn.modules.module.Module';
-            this.type = { name: type };
-            for (const [name, tensor] of pytorch.Graph._getParameters(module)) {
-                const initializer = initializers.get(tensor) || (tensor ? new pytorch.Tensor('', tensor) : null);
-                const value = values.map('', null, initializer || null);
-                this.inputs.push(new pytorch.Argument(name, [value]));
-                if (tensor.__variable__) {
-                    const value = values.map(tensor.__variable__);
-                    const argument = new pytorch.Argument(name, [value]);
-                    this.outputs.push(argument);
+        } else {
+            if (!type) {
+                if (pytorch.Utility.isInstance(obj, 'torch.jit._script.RecursiveScriptModule') && obj._c && obj._c.qualified_name) {
+                    type = obj._c.qualified_name;
+                } else if (pytorch.Utility.isInstance(obj, 'builtins.function')) {
+                    type = `${obj.__module__}.${obj.__name__}`;
+                    obj = {};
+                } else if (obj && obj.__class__ && obj.__class__.__module__ && obj.__class__.__name__) {
+                    type = `${obj.__class__.__module__}.${obj.__class__.__name__}`;
+                } else {
+                    type = 'builtins.object';
                 }
             }
-        } else {
-            this.type = type(metadata, item.type);
-            let obj = item.obj;
-            if (pytorch.Utility.isInstance(obj, 'builtins.function')) {
-                this.type = { name: `${obj.__module__}.${obj.__name__}` };
-                obj = {};
-            }
-            const parameters = new Map();
-            const entries = [];
-            const attributes = new Map();
+            this.type = createType(metadata, type);
             stack = stack || new Set();
             const weights = pytorch.Utility.weights(obj);
             if (weights) {
@@ -457,124 +436,117 @@ pytorch.Node = class {
                 this.type.name = type;
             } else if (obj && pytorch.Utility.isInstance(obj, 'fastai.data.core.DataLoaders')) {
                 // continue
-            } else if (obj && item.type === 'builtins.bytearray') {
+            } else if (obj && pytorch.Utility.isInstance(obj, '__torch__.torch.classes._nnapi.Compilation')) {
+                // continue
+            } else if (obj && type === 'builtins.bytearray') {
                 const argument = new pytorch.Argument('value', Array.from(obj), 'byte[]');
                 this.inputs.push(argument);
             } else if (obj) {
+                const inputs = new Map(Array.isArray(this.type.inputs) ? this.type.inputs.map((input) => [input.name, input]) : []);
                 const list = obj instanceof Map ? Array.from(obj) : Object.entries(obj);
                 for (const [name, value] of list) {
-                    if (name === '__class__' || name === '__hide__') {
+                    if (name === '__class__' || name === '__parent__' || name === '__name__') {
                         continue;
-                    } else if (name === '_parameters' && value instanceof Map) {
-                        for (const [name, parameter] of Array.from(value)) {
-                            parameters.set(name, parameter);
-                        }
-                    } else if (name === '_buffers' && value instanceof Map) {
-                        for (const [name, buffer] of Array.from(value)) {
-                            parameters.set(name, buffer);
+                    } else if (pytorch.Utility.isInstance(value, 'collections.OrderedDict') && value instanceof Map && value.size === 0) {
+                        continue;
+                    } else if (pytorch.Utility.isInstance(value, 'builtins.set') && value instanceof Set && value.size === 0) {
+                        continue;
+                    } else if (pytorch.Utility.isInstance(value, 'builtins.list') && Array.isArray(value) && value.length === 0) {
+                        continue;
+                    }
+                    const parameters = new Map();
+                    if ((name === '_parameters' || name === '_buffers') && value instanceof Map && value.size > 0) {
+                        for (const [name, obj] of Array.from(value)) {
+                            parameters.set(name, obj);
                         }
                     } else if (Array.isArray(value) && value.every((tensor) => pytorch.Utility.isTensor(tensor))) {
                         parameters.set(name, value);
                     } else if (pytorch.Utility.isTensor(value)) {
                         parameters.set(name, value);
-                    } else if (value && value.__class__ && value.__class__.__module__ === 'collections' && value.__class__.__name__ === 'OrderedDict' &&
-                        value instanceof Map && value.size === 0) {
-                        continue;
-                    } else if (value && value.__class__ && value.__class__.__module__ === 'builtins' && value.__class__.__name__ === 'set' &&
-                        value instanceof Set && value.size === 0) {
-                        continue;
-                    } else if (value && value.__class__ && value.__class__.__module__ === 'builtins' && value.__class__.__name__ === 'list' &&
-                        Array.isArray(value) && value.length === 0) {
+                    }
+                    if (parameters.size > 0) {
+                        for (const [name, value] of parameters) {
+                            const list = Array.isArray(value) ? value.map((item) => pytorch.Utility.toTensor(item)) : [pytorch.Utility.toTensor(value)];
+                            const visible = inputs.has(name) ? inputs.get(name).visible || true : true;
+                            const args = list.filter((value) => value !== null && !value.__origin__).map((value) => {
+                                const name = value && value.name ? value.name : '';
+                                const identifier = list.length === 1 && value && value.__name__ ? value.__name__ : name;
+                                let tensor = null;
+                                if (initializers && initializers.has(value)) {
+                                    tensor = initializers.get(value);
+                                } else {
+                                    value = value.__source__ ? value.__source__ : value;
+                                    tensor = value ? new pytorch.Tensor(identifier, value) : null;
+                                }
+                                return new pytorch.Value(identifier, null, null, tensor);
+                            });
+                            const argument = new pytorch.Argument(name, args, null, visible);
+                            this.inputs.push(argument);
+                            if (value && value.__variable__) {
+                                const argument = new pytorch.Argument(name, [values.map(value.__variable__)]);
+                                this.outputs.push(argument);
+                            }
+                        }
                         continue;
-                    } else {
-                        entries.push([name, value]);
                     }
-                }
-            }
-            for (const [name, value] of entries) {
-                if (!parameters.has(name)) {
-                    attributes.set(name, value);
-                }
-            }
-            const inputs = new Map(Array.isArray(this.type.inputs) ? this.type.inputs.map((input) => [input.name, input]) : []);
-            for (const [name, value] of parameters) {
-                const list = Array.isArray(value) ? value.map((item) => pytorch.Utility.toTensor(item)) : [pytorch.Utility.toTensor(value)];
-                const visible = inputs.has(name) ? inputs.get(name).visible || true : true;
-                const values = list.filter((value) => value !== null).map((value) => {
-                    const name = value && value.name ? value.name : '';
-                    const identifier = list.length === 1 && value && value.__name__ ? value.__name__ : name;
-                    const tensor = value ? new pytorch.Tensor(identifier, value) : null;
-                    return new pytorch.Value(identifier, null, null, tensor);
-                });
-                const argument = new pytorch.Argument(name, values, null, visible);
-                this.inputs.push(argument);
-            }
-            for (const [name, value] of attributes) {
-                const type = this.type.identifier;
-                if (pytorch.Utility.isTensor(value)) {
-                    const tensor = new pytorch.Tensor('', value);
-                    const argument = new pytorch.Argument(name, tensor, 'tensor');
-                    this.inputs.push(argument);
-                } else if (value && pytorch.Utility.isInstance(value, 'torch.dtype')) {
-                    const node = new pytorch.Node(metadata, { type: value.toString() });
-                    const argument = new pytorch.Argument(name, node, 'object');
-                    this.inputs.push(argument);
-                } else if (Array.isArray(value) && value.some((value) => pytorch.Utility.isTensor(value)) && value.every((value) => pytorch.Utility.isTensor(value) || value === null)) {
-                    const tensors = value.map((value) => value === null ? value : new pytorch.Tensor('', value));
-                    const argument = new pytorch.Argument(name, tensors, 'tensor[]');
-                    this.inputs.push(argument);
-                } else if (pytorch.Utility.isInstance(value, 'numpy.ndarray') || pytorch.Utility.isInstance(value, 'numpy.matrix')) {
-                    const tensor = new numpy.Tensor(value);
-                    const argument = new pytorch.Argument(name, tensor, 'tensor');
-                    this.inputs.push(argument);
-                } else if (Array.isArray(value) && value.every((value) => typeof value === 'string')) {
-                    const argument = new pytorch.Argument(name, value, 'string[]');
-                    this.inputs.push(argument);
-                } else if (Array.isArray(value) && value.every((value) => typeof value === 'number')) {
-                    const argument = new pytorch.Argument(name, value, 'attribute');
-                    this.inputs.push(argument);
-                } else if (name === '_modules' && value && value.__class__ && value.__class__.__module__ === 'collections' && value.__class__.__name__ === 'OrderedDict' &&
-                    value instanceof Map && Array.from(value).every(([, value]) => value === null || value.__class__)) {
-                    const values = Array.from(value).filter(([, value]) => !stack.has(value)).map(([name, obj]) => {
-                        stack.add(value);
-                        const type = obj === null ? 'builtins.NoneType' : `${obj.__class__.__module__}.${obj.__class__.__name__}`;
-                        const node = new pytorch.Node(metadata, { name, type, obj });
-                        stack.delete(value);
-                        return node;
-                    });
-                    const argument = new pytorch.Argument(name, values, 'object[]');
-                    this.inputs.push(argument);
-                } else if (value && Array.isArray(value) && value.length > 0 && value.every((obj) => Array.isArray(obj) && obj.every((item) => typeof item === 'string' || typeof item === 'number'))) {
-                    const argument = new pytorch.Argument(name, value, 'attribute');
-                    this.inputs.push(argument);
-                } else if (value && Array.isArray(value) && value.length > 0 && value.every((obj) => obj && (obj.__class__ || obj === Object(obj)))) {
-                    const values = value.filter((value) => !stack.has(value));
-                    const nodes = values.map((value) => {
+                    const type = this.type.identifier;
+                    if (pytorch.Utility.isTensor(value)) {
+                        const tensor = new pytorch.Tensor('', value);
+                        const argument = new pytorch.Argument(name, tensor, 'tensor');
+                        this.inputs.push(argument);
+                    } else if (value && pytorch.Utility.isInstance(value, 'torch.dtype')) {
+                        const node = new pytorch.Node(metadata, null, value.toString(), {});
+                        const argument = new pytorch.Argument(name, node, 'object');
+                        this.inputs.push(argument);
+                    } else if (Array.isArray(value) && value.some((value) => pytorch.Utility.isTensor(value)) && value.every((value) => pytorch.Utility.isTensor(value) || value === null)) {
+                        const tensors = value.map((value) => value === null ? value : new pytorch.Tensor('', value));
+                        const argument = new pytorch.Argument(name, tensors, 'tensor[]');
+                        this.inputs.push(argument);
+                    } else if (pytorch.Utility.isInstance(value, 'numpy.ndarray') || pytorch.Utility.isInstance(value, 'numpy.matrix')) {
+                        const tensor = new numpy.Tensor(value);
+                        const argument = new pytorch.Argument(name, tensor, 'tensor');
+                        this.inputs.push(argument);
+                    } else if (Array.isArray(value) && value.every((value) => typeof value === 'string')) {
+                        const argument = new pytorch.Argument(name, value, 'string[]');
+                        this.inputs.push(argument);
+                    } else if (Array.isArray(value) && value.every((value) => typeof value === 'number')) {
+                        const argument = new pytorch.Argument(name, value, 'attribute');
+                        this.inputs.push(argument);
+                    } else if (name === '_modules' && pytorch.Utility.isInstance(value, 'collections.OrderedDict') &&
+                        value instanceof Map && Array.from(value).every(([, value]) => value === null || value.__class__)) {
+                        const values = Array.from(value).filter(([, value]) => !stack.has(value)).map(([name, obj]) => {
+                            stack.add(value);
+                            const type = obj === null ? 'builtins.NoneType' : `${obj.__class__.__module__}.${obj.__class__.__name__}`;
+                            const node = new pytorch.Node(metadata, name, type, obj);
+                            stack.delete(value);
+                            return node;
+                        });
+                        const argument = new pytorch.Argument(name, values, 'object[]');
+                        this.inputs.push(argument);
+                    } else if (value && Array.isArray(value) && value.length > 0 && value.every((obj) => Array.isArray(obj) && obj.every((item) => typeof item === 'string' || typeof item === 'number'))) {
+                        const argument = new pytorch.Argument(name, value, 'attribute');
+                        this.inputs.push(argument);
+                    } else if (value && Array.isArray(value) && value.length > 0 && value.every((obj) => obj && (obj.__class__ || obj === Object(obj)))) {
+                        const list = value.filter((value) => !stack.has(value));
+                        const nodes = list.map((value) => {
+                            stack.add(value);
+                            const node = new pytorch.Node(metadata, null, null, value, initializers, values, stack);
+                            stack.delete(value);
+                            return node;
+                        });
+                        const argument = new pytorch.Argument(name, nodes, 'object[]');
+                        this.inputs.push(argument);
+                    } else if (value && (value.__class__ || typeof value === 'object') && !stack.has(value)) {
                         stack.add(value);
-                        const item = {
-                            type: value.__class__ ? `${value.__class__.__module__}.${value.__class__.__name__}` : 'builtins.object',
-                            obj: value
-                        };
-                        const node = new pytorch.Node(metadata, item, initializers, values, stack);
+                        const node = new pytorch.Node(metadata, null, null, value, initializers, values, stack);
                         stack.delete(value);
-                        return node;
-                    });
-                    const argument = new pytorch.Argument(name, nodes, 'object[]');
-                    this.inputs.push(argument);
-                } else if (value && (value.__class__ || typeof value === 'object') && !stack.has(value)) {
-                    stack.add(value);
-                    const item = {
-                        type: value.__class__ ? `${value.__class__.__module__}.${value.__class__.__name__}` : 'builtins.object',
-                        obj: value
-                    };
-                    const node = new pytorch.Node(metadata, item, initializers, values, stack);
-                    stack.delete(value);
-                    const visible = name === '_metadata' && pytorch.Utility.isMetadataObject(value) ? false : true;
-                    const argument = new pytorch.Argument(name, node, 'object', visible);
-                    this.inputs.push(argument);
-                } else {
-                    const argument = createAttribute(metadata.attribute(type, name), name, value);
-                    this.inputs.push(argument);
+                        const visible = name === '_metadata' && pytorch.Utility.isMetadataObject(value) ? false : true;
+                        const argument = new pytorch.Argument(name, node, 'object', visible);
+                        this.inputs.push(argument);
+                    } else {
+                        const argument = createAttribute(metadata.attribute(type, name), name, value);
+                        this.inputs.push(argument);
+                    }
                 }
             }
         }
@@ -2941,7 +2913,7 @@ pytorch.jit.ScriptModuleDeserializer = class {
             execution.builtins.CONSTANTS[`c${i}`] = constants[i];
         }
         const module = this.readArchive('data');
-        const result = new torch.ScriptModule();
+        const result = new torch.ScriptModule(`${module.__class__.__module__}.${module.__class__.__name__}`);
         result.data = module;
         return result;
     }
diff --git a/test/models.json b/test/models.json
index 6cced7ce04..189a7863eb 100644
--- a/test/models.json
+++ b/test/models.json
@@ -4923,6 +4923,7 @@
     "target":   "bad-base_libri.pt",
     "source":   "https://github.com/user-attachments/files/16401716/base_libri.pt.zip[base_libri.pt]",
     "format":   "PyTorch v1.6",
+    "assert":   "model.graphs[0].nodes[0].inputs[7].value.inputs[0].value.type.name == 'builtins.object' == null",
     "link":     "https://github.com/lutzroeder/netron/issues/543"
   },
   {
@@ -5372,7 +5373,7 @@
     "target":   "mnist_linear_dynamic_quantized.pt",
     "source":   "https://github.com/lutzroeder/netron/files/4774023/mnist_linear_dynamic_quantized.zip[mnist_linear_dynamic_quantized.pt]",
     "format":   "PyTorch v0.1.10",
-    "assert":   "model.graphs[0].nodes[0].inputs[3].value.type.name == 'torch.qint8'",
+    "assert":   "model.graphs[0].nodes[0].inputs[2].value.type.name == 'torch.qint8'",
     "link":     "https://github.com/lutzroeder/netron/issues/519"
   },
   {
@@ -5879,7 +5880,7 @@
     "target":   "rng_state.pth",
     "source":   "https://github.com/user-attachments/files/16401709/rng_state.pth.zip[rng_state.pth]",
     "format":   "PyTorch v1.6",
-    "assert":   "model.graphs[0].nodes[0].inputs[3].name == 'numpy'",
+    "assert":   "model.graphs[0].nodes[0].inputs[1].name == 'numpy'",
     "link":     "https://github.com/lutzroeder/netron/issues/543"
   },
   {
@@ -6266,7 +6267,6 @@
     "target":   "v1_lj_8000.jit",
     "source":   "https://github.com/user-attachments/files/16041474/v1_lj_8000.jit.zip[v1_lj_8000.jit]",
     "format":   "TorchScript v1.6",
-    "error":    "Cannot read properties of undefined (reading 'position')",
     "link":     "https://github.com/lutzroeder/netron/issues/1061"
   },
   {