diff --git a/src/torchscript-metadata.json b/src/torchscript-metadata.json
index f189d8f374b..1673972d971 100644
--- a/src/torchscript-metadata.json
+++ b/src/torchscript-metadata.json
@@ -44,6 +44,27 @@
       ]
     }
   },
+  {
+    "name": "conv2d_relu",
+    "schema": {
+      "category": "Layer",
+      "inputs": [
+        { "name": "input" },
+        { "name": "packed_weight" }
+      ],
+      "attributes": [
+        { "name": "stride", "type": "int64[]", "default": 1 },
+        { "name": "padding", "type": "int64[]", "default": 0 },
+        { "name": "dilation", "type": "int64[]", "default": 1 },
+        { "name": "groups", "type": "int64", "default": 1 },
+        { "name": "output_scale", "type": "float64" },
+        { "name": "output_zero_point", "type": "int64" }
+      ],
+      "outputs": [
+        { "name": "output" }
+      ]
+    }
+  },
   {
     "name": "max_pool2d",
     "schema": {
diff --git a/src/torchscript.js b/src/torchscript.js
index a62697fd7b2..f948541c987 100644
--- a/src/torchscript.js
+++ b/src/torchscript.js
@@ -1181,6 +1181,12 @@ torchscript.Container = class {
             }
             throw new torchscript.Error('Unknown expression type.');
         });
+        this._functionTable.set('torch.jit._pickle.build_boollist', function(data) {
+            return data;
+        });
+        this._functionTable.set('torch.jit._pickle.build_doublelist', function(data) {
+            return data;
+        });
         this._functionTable.set('torch.jit._pickle.build_intlist', function(data) {
             return data;
         });
@@ -1852,8 +1858,7 @@ torchscript.GraphContext = class {
     _nodeExpression(expression, target) {
         if (expression.type == 'call' && (target.type == 'id' || target.type == 'tuple')) {
             let name = torchscript.Utility.target(expression.target);
-            let namespace = 'torch.';
-            if (name.startsWith(namespace)) {
+            if (name.startsWith('torch.') || name.startsWith('ops.quantized')) {
                 let inputs = [];
                 let outputs = [];
                 let args = expression.arguments;
@@ -1956,12 +1961,30 @@ torchscript.GraphContext = class {
                 let attributes = [];
                 while (args.length > 0) {
                     let attributeExpression = args[0]; 
-                    if (attributeExpression.type == 'list') {
-                        for (let i = 0; i < attributeExpression.value.length; i++) {
-                            attributeExpression.value[i] = this._attributeExpression(attributeExpression.value[i]);
+                    if (this._isCall(attributeExpression, 'int', [ {} ]) ||
+                        this._isCall(attributeExpression, 'float', [ {} ])) {
+                        const tensor = this._evaluateExpression(attributeExpression.arguments[0]);
+                        if (tensor && tensor.size && tensor.size.length === 1 && tensor.size[0] === 1 &&
+                            tensor.storage && tensor.storage.data) {
+                            const dataView = new DataView(tensor.storage.data.buffer, tensor.storage.byteOffset, tensor.storage.byteLength);
+                            switch (tensor.dataType) {
+                                case 'float32': {
+                                    attributes.push(dataView.getFloat32(0, true));
+                                    break;
+                                }
+                                case 'int32': {
+                                    attributes.push(dataView.getInt32(0, true));
+                                    break;
+                                }
+                            }
+                            args.shift();
+                            continue;
                         }
                     }
                     let intExpression = this._attributeExpression(attributeExpression);
+                    if (intExpression.type == 'list' && intExpression.value.every((item) => item.type === 'number')) {
+                        intExpression = intExpression.value.map((item) => item.value); 
+                    }
                     if (intExpression) {
                         attributeExpression = intExpression;
                     }
@@ -1977,7 +2000,7 @@ torchscript.GraphContext = class {
                     }
                 }
                 this._nodes.push({
-                    name: name.substring(namespace.length),
+                    name: name.split('.').pop(),
                     attributes: attributes,
                     inputs: inputs,
                     outputs: outputs
@@ -2019,7 +2042,7 @@ torchscript.GraphContext = class {
     _attributeExpression(expression) {
         if (expression.type == 'id') {
             if (this._state[expression.value]) {
-                return this._state[expression.value];
+                return this._evaluateExpression(this._state[expression.value]);
             }
         }
         return this._evaluateExpression(expression);
@@ -2074,6 +2097,11 @@ torchscript.GraphContext = class {
                     this._state[target.value] = expression;
                     return true;
                 }
+                // _0 = torch.len(...)
+                if (this._isCall(expression, 'torch.len', [ {} ])) {
+                    this._state[target.value] = expression;
+                    return true;
+                }
                 // _output_size = torch.list_with_default([7, 7], torch.size(x0))
                 if (this._isCall(expression, 'torch.list_with_default', [ {}, {} ])) {
                     this._state[target.value] = expression;
@@ -2108,8 +2136,10 @@ torchscript.GraphContext = class {
                     return true;
                 }
                 const valueExpression = this._evaluateExpression(expression);
-                if (valueExpression.type === 'number' || this._isBooleanLiteral(valueExpression)) {
-                    this._state[target.value] = expression;
+                if (valueExpression.type === 'number' || 
+                    this._isBooleanLiteral(valueExpression) ||
+                    (valueExpression.type === 'list' && valueExpression.value.every((item) => item.type == 'number'))) {
+                    this._state[target.value] = valueExpression;
                     return true;
                 }
                 // _aux = None
@@ -2143,14 +2173,19 @@ torchscript.GraphContext = class {
                     }
                 }
             }
-            /*
-            if (target.type === 'tuple' && target.value.every((item) => item.type === 'id')) {
+            if (target.type === 'tuple' && 
+                target.value.every((item) => item.type === 'id')) {
                 // _30, _31, = _24
                 if (expression.type === 'id' && this._state[expression.value]) {
-                    debugger;
+                    const valueExpression = this._state[expression.value];
+                    if (valueExpression.type === 'list' && target.value.length === valueExpression.value.length) {
+                        for (let i = 0; i < target.value.length; i++) {
+                            this._state[target.value[i].value] = valueExpression.value[i];
+                        }
+                        return true;
+                    }
                 }
             }
-            */
         }
         return false;
     }
@@ -2355,11 +2390,19 @@ torchscript.GraphContext = class {
                 if (typeof value === 'number') {
                     return { type: 'number', value: value };
                 }
+                if (Array.isArray(value) && value.every((item) => typeof item === 'number')) {
+                    const array = value;
+                    return { type: 'list', value: array.map((item) => { return { type: 'number', value: item }; }) };
+                }
                 if (torchscript.Utility.isTensor(value)) {
                     return value;
                 }
             }
         }
+        if (expression.type === 'list') {
+            const value = expression.value.map((item) => this._evaluateExpression(item));
+            return { type: 'list', value: value };
+        }
         // int(x)
         if (this._isCall(expression, 'int', [ {} ])) {
             return this._evaluateExpression(expression.arguments[0]);
diff --git a/test/models.json b/test/models.json
index 4f0f8af6546..60608f63d6d 100644
--- a/test/models.json
+++ b/test/models.json
@@ -5143,75 +5143,74 @@
   },
   {
     "type":   "torchscript",
-    "target": "mnist_linear_torchscript_1.pt",
-    "source": "https://github.com/lutzroeder/netron/files/3585288/mnist_linear_torchscript.zip[mnist_linear_torchscript_1.pt]",
+    "target": "inception_v3.pt",
+    "script": [ "${root}/tools/pytorch", "sync install zoo" ],
     "format": "TorchScript v1",
-    "link":   "https://github.com/lutzroeder/netron/issues/281"
+    "link":   "https://pytorch.org/docs/stable/torchvision/models.html"
   },
   {
     "type":   "torchscript",
-    "target": "mnist_linear_torchscript_2.pt",
-    "source": "https://github.com/lutzroeder/netron/files/3585288/mnist_linear_torchscript.zip[mnist_linear_torchscript_2.pt]",
+    "target": "inception_v3_traced.pt",
+    "script": [ "${root}/tools/pytorch", "sync install zoo" ],
     "format": "TorchScript v1",
-    "link":   "https://github.com/lutzroeder/netron/issues/281"
+    "link":   "https://pytorch.org/docs/stable/torchvision/models.html"
   },
   {
     "type":   "torchscript",
-    "target": "mobilenet_quantized_scripted_925.pt",
-    "source": "https://github.com/pytorch/android-demo-app/blob/master/PyTorchDemoApp/app/src/main/assets/mobilenet_quantized_scripted_925.pt?raw=true",
-    "error":  "Unknown statement '{\"location\":\" at 374:8\",\"type\":\"=\",\"target\":{\"type\":\"id\",\"value\":\"_24\",\"keyword\":false},\"expression\":{\"location\":\" at 374:14\",\"type\":\".\",\"target\":{\"type\":\"id\",\"value\":\"_21\",\"keyword\":false},\"member\":{\"type\":\"id\",\"value\":\"stride\",\"keyword\":false}}}' in 'mobilenet_quantized_scripted_925.pt'.",
+    "target": "junction_mlp_vehicle_model.pt",
+    "source": "https://github.com/ApolloAuto/apollo/blob/master/modules/prediction/data/junction_mlp_vehicle_model.pt?raw=true",
     "format": "TorchScript v1",
-    "link":   "https://github.com/pytorch/android-demo-app"
+    "link":   "https://github.com/ApolloAuto/apollo"
   },
   {
     "type":   "torchscript",
-    "target": "model-reddit16-f140225004_2.pt1",
-    "source": "https://github.com/pytorch/android-demo-app/blob/master/PyTorchDemoApp/app/src/main/assets/model-reddit16-f140225004_2.pt1?raw=true",
-    "error":  "Unknown statement '{\"location\":\" at 132:17\",\"type\":\"=\",\"target\":{\"location\":\" at 132:11\",\"type\":\"tuple\",\"value\":[{\"type\":\"id\",\"value\":\"_32\",\"keyword\":false},{\"type\":\"id\",\"value\":\"_33\",\"keyword\":false}]},\"expression\":{\"type\":\"id\",\"value\":\"hx\",\"keyword\":false}}' in 'model-reddit16-f140225004_2.pt1'.",
+    "target": "lane_scanning_vehicle_model.pt",
+    "source": "https://github.com/ApolloAuto/apollo/blob/master/modules/prediction/data/lane_scanning_vehicle_model.pt?raw=true",
     "format": "TorchScript v1",
-    "link":   "https://github.com/pytorch/android-demo-app"
+    "link":   "https://github.com/ApolloAuto/apollo"
   },
   {
     "type":   "torchscript",
-    "target": "inception_v3.pt",
+    "target": "mobilenet_v2.pt",
     "script": [ "${root}/tools/pytorch", "sync install zoo" ],
     "format": "TorchScript v1",
     "link":   "https://pytorch.org/docs/stable/torchvision/models.html"
   },
   {
     "type":   "torchscript",
-    "target": "inception_v3_traced.pt",
-    "script": [ "${root}/tools/pytorch", "sync install zoo" ],
+    "target": "mnist_linear_torchscript_1.pt",
+    "source": "https://github.com/lutzroeder/netron/files/3585288/mnist_linear_torchscript.zip[mnist_linear_torchscript_1.pt]",
     "format": "TorchScript v1",
-    "link":   "https://pytorch.org/docs/stable/torchvision/models.html"
+    "link":   "https://github.com/lutzroeder/netron/issues/281"
   },
   {
     "type":   "torchscript",
-    "target": "junction_mlp_vehicle_model.pt",
-    "source": "https://github.com/ApolloAuto/apollo/blob/master/modules/prediction/data/junction_mlp_vehicle_model.pt?raw=true",
+    "target": "mnist_linear_torchscript_2.pt",
+    "source": "https://github.com/lutzroeder/netron/files/3585288/mnist_linear_torchscript.zip[mnist_linear_torchscript_2.pt]",
     "format": "TorchScript v1",
-    "link":   "https://github.com/ApolloAuto/apollo"
+    "link":   "https://github.com/lutzroeder/netron/issues/281"
   },
   {
     "type":   "torchscript",
-    "target": "lane_scanning_vehicle_model.pt",
-    "source": "https://github.com/ApolloAuto/apollo/blob/master/modules/prediction/data/lane_scanning_vehicle_model.pt?raw=true",
+    "target": "mobilenet_quantized_scripted_925.pt",
+    "source": "https://github.com/pytorch/android-demo-app/blob/master/PyTorchDemoApp/app/src/main/assets/mobilenet_quantized_scripted_925.pt?raw=true",
     "format": "TorchScript v1",
-    "link":   "https://github.com/ApolloAuto/apollo"
+    "link":   "https://github.com/pytorch/android-demo-app"
   },
   {
     "type":   "torchscript",
-    "target": "mobilenet_v2.pt",
+    "target": "mobilenet_v2_traced.pt",
     "script": [ "${root}/tools/pytorch", "sync install zoo" ],
     "format": "TorchScript v1",
     "link":   "https://pytorch.org/docs/stable/torchvision/models.html"
   },
   {
     "type":   "torchscript",
-    "target": "mobilenet_v2_traced.pt",
-    "script": [ "${root}/tools/pytorch", "sync install zoo" ],
+    "target": "model-reddit16-f140225004_2.pt1",
+    "source": "https://github.com/pytorch/android-demo-app/blob/master/PyTorchDemoApp/app/src/main/assets/model-reddit16-f140225004_2.pt1?raw=true",
+    "error":  "Unknown statement '{\"location\":\" at 132:17\",\"type\":\"=\",\"target\":{\"location\":\" at 132:11\",\"type\":\"tuple\",\"value\":[{\"type\":\"id\",\"value\":\"_32\",\"keyword\":false},{\"type\":\"id\",\"value\":\"_33\",\"keyword\":false}]},\"expression\":{\"type\":\"id\",\"value\":\"hx\",\"keyword\":false}}' in 'model-reddit16-f140225004_2.pt1'.",
     "format": "TorchScript v1",
-    "link":   "https://pytorch.org/docs/stable/torchvision/models.html"
+    "link":   "https://github.com/pytorch/android-demo-app"
   },
   {
     "type":   "torchscript",