diff --git a/package.json b/package.json
index d2370f34531..9763aa727a3 100755
--- a/package.json
+++ b/package.json
@@ -6,8 +6,8 @@
         "email": "lutzroeder@users.noreply.github.com",
         "url": "https://www.lutzroeder.com"
     },
-    "version": "7.1.4",
-    "date": "2023-08-17 21:47:36",
+    "version": "7.1.6",
+    "date": "2023-09-02 16:04:30",
     "description": "Visualizer for neural network, deep learning, and machine learning models",
     "license": "MIT",
     "repository": "lutzroeder/netron",
@@ -31,10 +31,10 @@
         "electron-updater": "6.1.1"
     },
     "devDependencies": {
-        "electron": "26.0.0",
+        "electron": "26.1.0",
         "electron-builder": "24.6.3",
         "@electron/notarize": "2.1.0",
-        "eslint": "8.47.0"
+        "eslint": "8.48.0"
     },
     "eslintConfig": {
         "env": {
diff --git a/source/bigdl.js b/source/bigdl.js
index 762692b2f45..8fa3ed3aa77 100644
--- a/source/bigdl.js
+++ b/source/bigdl.js
@@ -327,11 +327,11 @@ bigdl.Tensor = class {
                 case 'float32':
                     if (storage.bytes_data && storage.bytes_data.length > 0) {
                         this._values = storage.bytes_data[0];
-                        this._layout = '<';
+                        this._encoding = '<';
                     }
                     else if (storage.float_data && storage.float_data.length > 0) {
                         this._values = storage.float_data;
-                        this._layout = '|';
+                        this._encoding = '|';
                     }
                     break;
                 default:
@@ -349,8 +349,8 @@ bigdl.Tensor = class {
         return this._type;
     }
 
-    get layout() {
-        return this._layout;
+    get encoding() {
+        return this._encoding;
     }
 
     get values() {
diff --git a/source/caffe.js b/source/caffe.js
index 0f93b6a1a82..3698c3ccf7b 100644
--- a/source/caffe.js
+++ b/source/caffe.js
@@ -627,7 +627,7 @@ caffe.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         return '|';
     }
 
diff --git a/source/caffe2.js b/source/caffe2.js
index cd19db81d84..d560dd7255b 100644
--- a/source/caffe2.js
+++ b/source/caffe2.js
@@ -561,7 +561,7 @@ caffe2.Tensor = class {
         return null;
     }
 
-    get layout() {
+    get encoding() {
         return '|';
     }
 
diff --git a/source/circle-metadata.json b/source/circle-metadata.json
index d4e0efb4092..cfcf0ad24e6 100644
--- a/source/circle-metadata.json
+++ b/source/circle-metadata.json
@@ -394,7 +394,8 @@
     "attributes": [
       { "name": "padding", "type": "Padding", "default": "SAME" },
       { "name": "stride_w", "type": "int32", "default": 0 },
-      { "name": "stride_h", "type": "int32", "default": 0 }
+      { "name": "stride_h", "type": "int32", "default": 0 },
+      { "name": "fused_activation_function", "type": "ActivationFunctionType", "default": "NONE" }
     ]
   },
   {
@@ -404,7 +405,8 @@
       { "name": "cell_clip", "type": "float32", "default": 0 },
       { "name": "proj_clip", "type": "float32", "default": 0 },
       { "name": "time_major", "type": "boolean", "default": false },
-      { "name": "asymmetric_quantize_inputs", "type": "boolean", "default": false }
+      { "name": "asymmetric_quantize_inputs", "type": "boolean", "default": false },
+      { "name": "diagonal_recurrent_tensors", "type": "boolean", "default": false }
     ]
   },
   {
diff --git a/source/circle-schema.js b/source/circle-schema.js
index 3b4f767c3ea..aff88ffed8d 100644
--- a/source/circle-schema.js
+++ b/source/circle-schema.js
@@ -19,7 +19,8 @@ $root.circle.TensorType = {
     RESOURCE: 13,
     VARIANT: 14,
     UINT32: 15,
-    UINT16: 16
+    UINT16: 16,
+    INT4: 17
 };
 
 $root.circle.CustomQuantization = class CustomQuantization {
@@ -190,6 +191,25 @@ $root.circle.SparsityParameters = class SparsityParameters {
     }
 };
 
+$root.circle.VariantSubType = class VariantSubType {
+
+    static decode(reader, position) {
+        const $ = new $root.circle.VariantSubType();
+        $.shape = reader.typedArray(position, 4, Int32Array);
+        $.type = reader.int8_(position, 6, 0);
+        $.has_rank = reader.bool_(position, 8, false);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.circle.VariantSubType();
+        $.shape = reader.typedArray(json.shape, Int32Array);
+        $.type = $root.circle.TensorType[json.type];
+        $.has_rank = reader.value(json.has_rank, false);
+        return $;
+    }
+};
+
 $root.circle.Tensor = class Tensor {
 
     static decode(reader, position) {
@@ -203,6 +223,7 @@ $root.circle.Tensor = class Tensor {
         $.sparsity = reader.table(position, 16, $root.circle.SparsityParameters.decode);
         $.shape_signature = reader.typedArray(position, 18, Int32Array);
         $.has_rank = reader.bool_(position, 20, false);
+        $.variant_tensors = reader.tableArray(position, 22, $root.circle.VariantSubType.decode);
         return $;
     }
 
@@ -217,6 +238,7 @@ $root.circle.Tensor = class Tensor {
         $.sparsity = reader.object(json.sparsity, $root.circle.SparsityParameters.decodeText);
         $.shape_signature = reader.typedArray(json.shape_signature, Int32Array);
         $.has_rank = reader.value(json.has_rank, false);
+        $.variant_tensors = reader.objectArray(json.variant_tensors, $root.circle.VariantSubType.decodeText);
         return $;
     }
 };
@@ -381,7 +403,12 @@ $root.circle.BuiltinOperator = {
     UNSORTED_SEGMENT_PROD: 153,
     UNSORTED_SEGMENT_MAX: 154,
     UNSORTED_SEGMENT_SUM: 155,
-    ATAN2: 156
+    ATAN2: 156,
+    UNSORTED_SEGMENT_MIN: 157,
+    SIGN: 158,
+    BITCAST: 159,
+    BITWISE_XOR: 160,
+    RIGHT_SHIFT: 161
 };
 
 $root.circle.BuiltinOptions = class {
@@ -507,8 +534,13 @@ $root.circle.BuiltinOptions = class {
             case 117: return $root.circle.DynamicUpdateSliceOptions.decode(reader, position);
             case 118: return $root.circle.UnsortedSegmentProdOptions.decode(reader, position);
             case 119: return $root.circle.UnsortedSegmentMaxOptions.decode(reader, position);
-            case 120: return $root.circle.UnsortedSegmentSumOptions.decode(reader, position);
-            case 121: return $root.circle.ATan2Options.decode(reader, position);
+            case 120: return $root.circle.UnsortedSegmentMinOptions.decode(reader, position);
+            case 121: return $root.circle.UnsortedSegmentSumOptions.decode(reader, position);
+            case 122: return $root.circle.ATan2Options.decode(reader, position);
+            case 123: return $root.circle.SignOptions.decode(reader, position);
+            case 124: return $root.circle.BitcastOptions.decode(reader, position);
+            case 125: return $root.circle.BitwiseXorOptions.decode(reader, position);
+            case 126: return $root.circle.RightShiftOptions.decode(reader, position);
             case 252: return $root.circle.BCQGatherOptions.decode(reader, position);
             case 253: return $root.circle.BCQFullyConnectedOptions.decode(reader, position);
             case 254: return $root.circle.InstanceNormOptions.decode(reader, position);
@@ -637,8 +669,13 @@ $root.circle.BuiltinOptions = class {
             case 'DynamicUpdateSliceOptions': return $root.circle.DynamicUpdateSliceOptions.decodeText(reader, json);
             case 'UnsortedSegmentProdOptions': return $root.circle.UnsortedSegmentProdOptions.decodeText(reader, json);
             case 'UnsortedSegmentMaxOptions': return $root.circle.UnsortedSegmentMaxOptions.decodeText(reader, json);
+            case 'UnsortedSegmentMinOptions': return $root.circle.UnsortedSegmentMinOptions.decodeText(reader, json);
             case 'UnsortedSegmentSumOptions': return $root.circle.UnsortedSegmentSumOptions.decodeText(reader, json);
             case 'ATan2Options': return $root.circle.ATan2Options.decodeText(reader, json);
+            case 'SignOptions': return $root.circle.SignOptions.decodeText(reader, json);
+            case 'BitcastOptions': return $root.circle.BitcastOptions.decodeText(reader, json);
+            case 'BitwiseXorOptions': return $root.circle.BitwiseXorOptions.decodeText(reader, json);
+            case 'RightShiftOptions': return $root.circle.RightShiftOptions.decodeText(reader, json);
             case 'BCQGatherOptions': return $root.circle.BCQGatherOptions.decodeText(reader, json);
             case 'BCQFullyConnectedOptions': return $root.circle.BCQFullyConnectedOptions.decodeText(reader, json);
             case 'InstanceNormOptions': return $root.circle.InstanceNormOptions.decodeText(reader, json);
@@ -1047,6 +1084,7 @@ $root.circle.UnidirectionalSequenceLSTMOptions = class UnidirectionalSequenceLST
         $.proj_clip = reader.float32_(position, 8, 0);
         $.time_major = reader.bool_(position, 10, false);
         $.asymmetric_quantize_inputs = reader.bool_(position, 12, false);
+        $.diagonal_recurrent_tensors = reader.bool_(position, 14, false);
         return $;
     }
 
@@ -1057,6 +1095,7 @@ $root.circle.UnidirectionalSequenceLSTMOptions = class UnidirectionalSequenceLST
         $.proj_clip = reader.value(json.proj_clip, 0);
         $.time_major = reader.value(json.time_major, false);
         $.asymmetric_quantize_inputs = reader.value(json.asymmetric_quantize_inputs, false);
+        $.diagonal_recurrent_tensors = reader.value(json.diagonal_recurrent_tensors, false);
         return $;
     }
 };
@@ -1657,6 +1696,7 @@ $root.circle.TransposeConvOptions = class TransposeConvOptions {
         $.padding = reader.int8_(position, 4, 0);
         $.stride_w = reader.int32_(position, 6, 0);
         $.stride_h = reader.int32_(position, 8, 0);
+        $.fused_activation_function = reader.int8_(position, 10, 0);
         return $;
     }
 
@@ -1665,6 +1705,7 @@ $root.circle.TransposeConvOptions = class TransposeConvOptions {
         $.padding = $root.circle.Padding[json.padding];
         $.stride_w = reader.value(json.stride_w, 0);
         $.stride_h = reader.value(json.stride_h, 0);
+        $.fused_activation_function = $root.circle.ActivationFunctionType[json.fused_activation_function];
         return $;
     }
 };
@@ -2550,6 +2591,71 @@ $root.circle.ATan2Options = class ATan2Options {
     }
 };
 
+$root.circle.UnsortedSegmentMinOptions = class UnsortedSegmentMinOptions {
+
+    static decode(/* reader, position */) {
+        const $ = new $root.circle.UnsortedSegmentMinOptions();
+        return $;
+    }
+
+    static decodeText(/* reader, json */) {
+        const $ = new $root.circle.UnsortedSegmentMinOptions();
+        return $;
+    }
+};
+
+$root.circle.SignOptions = class SignOptions {
+
+    static decode(/* reader, position */) {
+        const $ = new $root.circle.SignOptions();
+        return $;
+    }
+
+    static decodeText(/* reader, json */) {
+        const $ = new $root.circle.SignOptions();
+        return $;
+    }
+};
+
+$root.circle.BitcastOptions = class BitcastOptions {
+
+    static decode(/* reader, position */) {
+        const $ = new $root.circle.BitcastOptions();
+        return $;
+    }
+
+    static decodeText(/* reader, json */) {
+        const $ = new $root.circle.BitcastOptions();
+        return $;
+    }
+};
+
+$root.circle.BitwiseXorOptions = class BitwiseXorOptions {
+
+    static decode(/* reader, position */) {
+        const $ = new $root.circle.BitwiseXorOptions();
+        return $;
+    }
+
+    static decodeText(/* reader, json */) {
+        const $ = new $root.circle.BitwiseXorOptions();
+        return $;
+    }
+};
+
+$root.circle.RightShiftOptions = class RightShiftOptions {
+
+    static decode(/* reader, position */) {
+        const $ = new $root.circle.RightShiftOptions();
+        return $;
+    }
+
+    static decodeText(/* reader, json */) {
+        const $ = new $root.circle.RightShiftOptions();
+        return $;
+    }
+};
+
 $root.circle.BCQGatherOptions = class BCQGatherOptions {
 
     static decode(reader, position) {
diff --git a/source/circle.js b/source/circle.js
index 4b25b67c050..65e9fe086a8 100644
--- a/source/circle.js
+++ b/source/circle.js
@@ -555,7 +555,7 @@ circle.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         switch (this._type.dataType) {
             case 'string': return '|';
             default: return '<';
diff --git a/source/cntk.js b/source/cntk.js
index 2b56d79b7b1..aafbecc412b 100644
--- a/source/cntk.js
+++ b/source/cntk.js
@@ -545,7 +545,7 @@ cntk.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         return '|';
     }
 
diff --git a/source/coreml.js b/source/coreml.js
index 1aeed032f37..37af5d71185 100644
--- a/source/coreml.js
+++ b/source/coreml.js
@@ -687,7 +687,7 @@ coreml.Graph = class {
                 }
             }
         };
-        const loadModel = (model, values, group, weights) => {
+        const loadModel = (model, values, group) => {
             this._groups = this._groups | (group.length > 0 ? true : false);
             const description = model && model.description && model.description.metadata && model.description.metadata.shortDescription ? model.description.metadata.shortDescription : '';
             switch (model.Type) {
@@ -1000,7 +1000,7 @@ coreml.Graph = class {
                 return new coreml.Argument(input.name, true, [ value ]);
             });
         }
-        this._type = loadModel(model, values, '', weights);
+        this._type = loadModel(model, values, '');
         if (this._description) {
             this._outputs = this._description.output.map((output) => {
                 const value = values.input(output.name);
@@ -1240,7 +1240,7 @@ coreml.Tensor = class {
         return null;
     }
 
-    get layout() {
+    get encoding() {
         switch (this._type.dataType) {
             case 'float32': return '|';
             default: return '<';
diff --git a/source/dlc.js b/source/dlc.js
index 3111d299e26..33c5af45445 100644
--- a/source/dlc.js
+++ b/source/dlc.js
@@ -243,10 +243,10 @@ dlc.Tensor = class {
     constructor(type, data) {
         this.type = type;
         if (data instanceof Uint8Array) {
-            this.layout = '<';
+            this.encoding = '<';
             this.values = data;
         } else {
-            this.layout = '|';
+            this.encoding = '|';
             switch (type.dataType) {
                 case 'uint8': this.values = data.bytes; break;
                 case 'float32': this.values = data.floats; break;
@@ -360,6 +360,7 @@ dlc.Container = class {
                                 case 0x0308: return 'qint8';
                                 case 0x0332: return 'qint32';
                                 case 0x0408: return 'uint8';
+                                case 0x0416: return 'uint16';
                                 case 0x0508: return 'boolean';
                                 default: throw new dlc.Error("Unsupported data type '" + JSON.stringify(value) + "'.");
                             }
diff --git a/source/flax.js b/source/flax.js
index 2d3da57332a..9b46a395c52 100644
--- a/source/flax.js
+++ b/source/flax.js
@@ -256,7 +256,7 @@ flax.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         switch (this._type.dataType) {
             case 'string':
             case 'object':
diff --git a/source/grapher.js b/source/grapher.js
index d2b9893ad8e..ffb4cec2c0e 100644
--- a/source/grapher.js
+++ b/source/grapher.js
@@ -253,12 +253,17 @@ grapher.Node = class {
     }
 
     select() {
-        this.element.classList.add('select');
-        return [ this.element ];
+        if (this.element) {
+            this.element.classList.add('select');
+            return [ this.element ];
+        }
+        return [];
     }
 
     deselect() {
-        this.element.classList.remove('select');
+        if (this.element) {
+            this.element.classList.remove('select');
+        }
     }
 
     static roundedRect(x, y, width, height, r1, r2, r3, r4) {
diff --git a/source/hickle.js b/source/hickle.js
index 0ffde6c5128..1b6b0c1326b 100644
--- a/source/hickle.js
+++ b/source/hickle.js
@@ -196,7 +196,7 @@ hickle.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         return this._littleEndian ? '<' : '>';
     }
 
diff --git a/source/index.html b/source/index.html
index 0231db2a74f..40b3265c870 100644
--- a/source/index.html
+++ b/source/index.html
@@ -92,9 +92,9 @@
 .titlebar-button { display: flex; justify-content: center; align-items: center; width: 46px; height: 32px; user-select: none; -webkit-app-region: no-drag; }
 .titlebar-button:hover { color: #000000; background-color: rgba(0, 0, 0, 0.15); }
 .titlebar-button-close:hover { color: #ffffff; background-color: #b43029; }
-.menu-button { display: flex; justify-content: center; align-items: center; color: #aaaaaa; font-size: 16px; height: 32px; width: 32px; position: fixed; top: 0; left: 0; right: 0; bottom: 0; z-index: 2; -webkit-app-region: no-drag; -webkit-app-region: no-drag; user-select: none; }
+.menu-button { display: flex; justify-content: center; align-items: center; color: #aaaaaa; font-size: 20px; height: 32px; width: 32px; position: fixed; top: 0; left: 0; right: 0; bottom: 0; z-index: 2; -webkit-app-region: no-drag; -webkit-app-region: no-drag; user-select: none; }
 .menu-button:hover { color: #000000; }
-.menu { display: block; position: absolute; left: -16em; width: 16em; top: 0; height: 100%; z-index: 2; background-color: #ececec; border-right: 1px solid rgba(255, 255, 255, 0.5); padding-top: 40px; padding-bottom: 2px; margin-left: 0; margin-top: 0; overflow: hidden; transition: 0.1s; }
+.menu { display: block; position: absolute; left: -17em; width: 17em; top: 0; height: 100%; z-index: 2; background-color: #ececec; border-right: 1px solid rgba(255, 255, 255, 0.5); padding-top: 40px; padding-bottom: 2px; margin-left: 0; margin-top: 0; overflow: hidden; transition: 0.1s; }
 .menu .menu-group { margin-bottom: 12px; }
 .menu .menu-group .menu-group-header { display: block; border: none; border-radius: 0; color: black; width: 100%; text-align: left; margin: 4px 12px 5px 12px; white-space: no-wrap; font-size: 11px; font-weight: bold; color: #bbbbbb; white-space: nowrap; }
 .menu .menu-group .menu-command { display: block; border: none; border-radius: 0; background-color: transparent; color: black; width: 100%; text-align: left; padding: 4px 12px 5px 12px; font-size: 12px; }
@@ -496,6 +496,6 @@ <h1 id="sidebar-title" class="sidebar-title"></h1>
     </div>
 </div>
 <div id="menu" class="menu"></div>
-<div id="menu-button" class="menu-button">&#9776;</div>
+<div id="menu-button" class="menu-button">&#8801;</div>
 </body>
 </html>
\ No newline at end of file
diff --git a/source/keras.js b/source/keras.js
index 34b58df0041..ac1eaf377bc 100644
--- a/source/keras.js
+++ b/source/keras.js
@@ -126,8 +126,8 @@ keras.ModelFactory = class {
                                         const components = weight_name.split('/');
                                         components.pop();
                                         const name = (components.length == 0 || components[0] !== layer_name) ? [ layer_name ].concat(components).join('/') : components.join('/');
-                                        const layout = variable.littleEndian ? '<' : '>';
-                                        const tensor = new keras.Tensor(weight_name, variable.shape, variable.type, null, layout, variable.data);
+                                        const encoding = variable.littleEndian ? '<' : '>';
+                                        const tensor = new keras.Tensor(weight_name, variable.shape, variable.type, null, encoding, variable.data);
                                         weights.add(name, tensor);
                                     }
                                 }
@@ -336,23 +336,23 @@ keras.Graph = class {
         this._outputs = [];
         this._nodes = [];
         group = group || '';
-        const args = new Map();
-        const arg = (name, type, tensor) => {
+        const values = new Map();
+        const value = (name, type, tensor) => {
             if (tensor) {
                 return new keras.Value(name, type || null, tensor);
             }
-            if (!args.has(name)) {
-                args.set(name, new keras.Value(name, type || null, tensor || null));
+            if (!values.has(name)) {
+                values.set(name, new keras.Value(name, type || null, tensor || null));
             } else if (type || tensor) {
                 throw new keras.Error("Duplicate value '" + name + "'.");
             }
-            return args.get(name);
+            return values.get(name);
         };
         const loadNode = (layer, inputs, outputs, weights, group) => {
             layer = Object.assign({}, layer);
             layer.inputs = inputs;
             layer.outputs = outputs;
-            return new keras.Node(this._metadata, layer, group, weights, arg);
+            return new keras.Node(this._metadata, layer, group, weights, value);
         };
         const getInputType = (layer) => {
             if (layer && layer.config) {
@@ -386,32 +386,34 @@ keras.Graph = class {
                     const outputs = null;
                     const inputName = 'input';
                     let inputType = null;
-                    let value = inputName;
+                    let name = inputName;
                     let index = 0;
                     const layers = config.layers ? config.layers : config;
                     for (const layer of layers) {
-                        let name = index.toString();
-                        const nodeInputs = [ { name: value } ];
+                        let current = index.toString();
+                        const nodeInputs = [ { name: name } ];
                         if (index == 0) {
                             inputType = getInputType(layer);
-                            this._inputs.push(new keras.Argument(inputName, true, [ arg(inputName, inputType) ]));
+                            const argument = new keras.Argument(inputName, true, [ value(inputName, inputType) ]);
+                            this._inputs.push(argument);
                         }
                         index++;
                         if (layer.config && layer.config.name) {
-                            name = layer.config.name;
+                            current = layer.config.name;
                         }
-                        value = name;
-                        let nodeOutputs = [ value ];
+                        name = current;
+                        let nodeOutputs = [ name ];
                         if (index == layers.length) {
                             if (outputs && outputs.length > 0) {
                                 nodeOutputs = [ outputs[0] ];
-                                value = null;
+                                name = null;
                             }
                         }
                         this.nodes.push(loadNode(layer, nodeInputs, nodeOutputs, weights, group));
                     }
-                    if (value) {
-                        this._outputs.push(new keras.Argument(value, true, [ arg(value) ]));
+                    if (name) {
+                        const argument = new keras.Argument(name, true, [ value(name) ]);
+                        this._outputs.push(argument);
                     }
                     break;
                 }
@@ -523,7 +525,7 @@ keras.Graph = class {
                                 type = getInputType(node);
                                 nodes.delete(name);
                             }
-                            const argument = new keras.Argument(name, true, [ arg(name, type) ]);
+                            const argument = new keras.Argument(name, true, [ value(name, type) ]);
                             this._inputs.push(argument);
                         }
                     }
@@ -531,19 +533,19 @@ keras.Graph = class {
                     if (output_layers) {
                         for (let j = 0; j < output_layers.length; j++) {
                             const output_layer = output_layers[j];
-                            let outputName = output_layer[0];
-                            const outputNode = nodes.get(outputName);
+                            let name = output_layer[0];
+                            const outputNode = nodes.get(name);
                             if (outputNode) {
                                 const outputIndex = output_layer[2];
                                 if (outputIndex != 0) {
-                                    outputName += ':' + outputIndex.toString();
+                                    name += ':' + outputIndex.toString();
                                 }
                                 while (outputIndex >= outputNode.outputs.length) {
                                     outputNode.outputs.push('');
                                 }
-                                outputNode.outputs[outputIndex] = outputName;
+                                outputNode.outputs[outputIndex] = name;
                             }
-                            const argument = new keras.Argument(outputName, true, [ arg(outputName) ]);
+                            const argument = new keras.Argument(name, true, [ value(name) ]);
                             this._outputs.push(argument);
                         }
                     }
@@ -563,7 +565,7 @@ keras.Graph = class {
             for (const name of weights.keys()) {
                 if (weights.get('', name).length <= 6) {
                     const layer = { class_name: 'Weights', config: { name: name } };
-                    const node = new keras.Node(metadata, layer, '', weights, arg);
+                    const node = new keras.Node(metadata, layer, '', weights, value);
                     this._nodes.push(node);
                 }
             }
@@ -644,7 +646,7 @@ keras.Value = class {
 
 keras.Node = class {
 
-    constructor(metadata, layer, group, weights, arg) {
+    constructor(metadata, layer, group, weights, value) {
         const config = layer.config || {};
         const args = layer.args || {};
         let inputs = layer.inputs || [];
@@ -671,18 +673,19 @@ keras.Node = class {
                     delete config.input_layers;
                     delete config.output_layers;
                 }
-                this._inputs = [ new keras.Argument('inputs', true, inputs.map((input) => arg(input.name))) ];
-                this._outputs = [ new keras.Argument('outputs', true, outputs.map((name) => arg(name))) ];
+                this._inputs = [ new keras.Argument('inputs', true, inputs.map((input) => value(input.name))) ];
+                this._outputs = [ new keras.Argument('outputs', true, outputs.map((name) => value(name))) ];
                 inputs = [];
                 outputs = [];
                 break;
             }
+            case 'Wrapper':
             case 'Bidirectional':
             case 'TimeDistributed': {
                 if (config && config.layer) {
                     const inner = config.layer;
                     delete config.layer;
-                    this._inner = new keras.Node(metadata, inner, null, null, arg);
+                    this._inner = new keras.Node(metadata, inner, null, null, value);
                     if (type == 'Bidirectional' && inner.config.name) {
                         names = [ name + '/forward_' + inner.config.name, name + '/backward_' + inner.config.name ];
                         if (!group) {
@@ -734,13 +737,13 @@ keras.Node = class {
                     if (typeof value === 'string') {
                         const set = new Map([ [ 'elu', 'ELU' ], [ 'exponential', 'Exponential' ], [ 'hard_sigmoid', 'HardSigmoid' ], [ 'linear', 'Linear' ], [ 'relu', 'ReLU' ], [ 'selu', 'SELU' ], [ 'softmax', 'Softmax'], [ 'sigmoid', 'Sigmoid' ], [ 'softplus', 'SoftPlus' ], [ 'softsign', 'SoftSign' ], [ 'tanh', 'TanH' ] ]);
                         const type = set.has(value) ? set.get(value) : value;
-                        this.chain.push(new keras.Node(metadata, { class_name: type }, null, null, arg));
+                        this.chain.push(new keras.Node(metadata, { class_name: type }, null, null, value));
                     } else if (value && typeof value.class_name === 'string' && value.config) {
                         const type = value.class_name;
                         if (!metadata.type(type)) {
                             metadata.add(type, { name: type, category: 'Activation' });
                         }
-                        this.chain.push(new keras.Node(metadata, value, null, null, arg));
+                        this.chain.push(new keras.Node(metadata, value, null, null, value));
                     }
                 }
                 if (name !== 'name') {
@@ -751,17 +754,17 @@ keras.Node = class {
         }
 
         const innerType = this.inner ? this.inner.type : null;
-        const innerSchema = innerType ? metadata.type(innerType) : null;
+        const innerMetadata = innerType ? metadata.type(innerType) : null;
         let inputIndex = 0;
         while (inputs.length > 0) {
             let list = false;
-            let inputName = null;
+            let name = null;
             let visible = true;
-            if (!innerSchema || inputIndex == 0) {
+            if (!innerMetadata || inputIndex == 0) {
                 if (this._type && this._type.inputs && inputIndex < this._type.inputs.length) {
                     const input = this._type.inputs[inputIndex];
-                    inputName = input.name;
-                    if (type === 'BatchNormalization' && inputName === 'gamma' && config.scale === false) {
+                    name = input.name;
+                    if (type === 'BatchNormalization' && name === 'gamma' && config.scale === false) {
                         inputIndex++;
                         continue;
                     }
@@ -774,13 +777,13 @@ keras.Node = class {
                 switch (type) {
                     case 'Bidirectional': {
                         let innerIndex = inputIndex;
-                        if (innerSchema && innerSchema.inputs) {
-                            if (innerIndex < innerSchema.inputs.length) {
-                                inputName = 'forward_' + innerSchema.inputs[innerIndex].name;
+                        if (innerMetadata && innerMetadata.inputs) {
+                            if (innerIndex < innerMetadata.inputs.length) {
+                                name = 'forward_' + innerMetadata.inputs[innerIndex].name;
                             } else {
-                                innerIndex = innerIndex - innerSchema.inputs.length + 1;
-                                if (innerIndex < innerSchema.inputs.length) {
-                                    inputName = 'backward_' + innerSchema.inputs[innerIndex].name;
+                                innerIndex = innerIndex - innerMetadata.inputs.length + 1;
+                                if (innerIndex < innerMetadata.inputs.length) {
+                                    name = 'backward_' + innerMetadata.inputs[innerIndex].name;
                                 }
                             }
                         }
@@ -788,8 +791,8 @@ keras.Node = class {
                         break;
                     }
                     case 'TimeDistributed':
-                        if (innerSchema && innerSchema.inputs && inputIndex < innerSchema.inputs.length) {
-                            inputName = innerSchema.inputs[inputIndex].name;
+                        if (innerMetadata && innerMetadata.inputs && inputIndex < innerMetadata.inputs.length) {
+                            name = innerMetadata.inputs[inputIndex].name;
                         }
                         break;
                     default:
@@ -799,33 +802,34 @@ keras.Node = class {
             const input = !list ? [ inputs.shift() ] : inputs.splice(0, inputs.length);
             const inputArguments = input.map((input) => {
                 if (input.name) {
-                    return arg(input.name, null, initializers[input.name]);
+                    return value(input.name, null, initializers[input.name]);
                 }
                 if (input.value !== undefined) {
                     const tensor = new keras.Tensor('', input.shape, config.dtype || '?', null, '|', input.value);
-                    return arg('', null, tensor);
+                    return value('', null, tensor);
                 }
                 throw new keras.Error("Invalid argument '" + JSON.stringify(input.name) + "'.");
             });
-            if (!inputName && inputArguments.length == 1 && inputArguments[0].initializer && inputArguments[0].initializer.name) {
+            if (!name && inputArguments.length == 1 && inputArguments[0].initializer && inputArguments[0].initializer.name) {
                 if (names.length === 1 && names[0] === '') {
-                    inputName = inputArguments[0].initializer.name;
+                    name = inputArguments[0].initializer.name;
                 } else {
                     const parts = inputArguments[0].initializer.name.split('/').pop().split(':').shift().split('_');
                     const inputName1 = parts.pop();
                     const inputName2 = parts.length > 0 ? [ parts.pop(), inputName1 ].join('_') : '';
                     const inputNames = new Set([ 'recurrent_kernel', 'running_mean', 'running_std', 'moving_mean', 'moving_variance', 'depthwise_filter', 'pointwise_filter' ]);
-                    inputName = inputNames.has(inputName2) ? inputName2 : inputName1;
+                    name = inputNames.has(inputName2) ? inputName2 : inputName1;
                 }
             }
-            this._inputs.push(new keras.Argument(inputName || inputIndex.toString(), visible, inputArguments));
+            const argument = new keras.Argument(name || inputIndex.toString(), visible, inputArguments);
+            this._inputs.push(argument);
             inputIndex++;
         }
 
         for (let i = 0; i < outputs.length; i++) {
             const output = outputs[i];
             const outputName = (this._type && this._type.outputs && i < this._type.outputs.length && this._type.outputs[i] && this._type.outputs[i].name) ? this._type.outputs[i].name : i.toString();
-            const args = output.length === 0 ? [] : [ arg(output) ];
+            const args = output.length === 0 ? [] : [ value(output) ];
             const argument = new keras.Argument(outputName, true, args);
             this._outputs.push(argument);
         }
@@ -833,19 +837,19 @@ keras.Node = class {
         const inputTypes = new Map((this._type.inputs || []).map((input) => [ input.name, input.type ]));
         for (const entry of Object.entries(args)) {
             const name = entry[0];
-            const value = entry[1];
+            const arg = entry[1];
             if (name !== 'name') {
-                if (value.name || (inputTypes.has(name) && inputTypes.get(name) === 'Tensor' && value)) {
-                    if (value.name) {
-                        const argument = new keras.Argument(name, true, [ arg(value.name) ]);
+                if (arg.name || (inputTypes.has(name) && inputTypes.get(name) === 'Tensor' && arg)) {
+                    if (arg.name) {
+                        const argument = new keras.Argument(name, true, [ value(arg.name) ]);
                         this._inputs.push(argument);
                     } else {
-                        const tensor = new keras.Tensor('', value.shape, config.dtype || '?', null, '|', value.value);
-                        const argument = new keras.Argument(name, true, [ arg('', null, tensor) ]);
+                        const tensor = new keras.Tensor('', arg.shape, config.dtype || '?', null, '|', arg.value);
+                        const argument = new keras.Argument(name, true, [ value('', null, tensor) ]);
                         this._inputs.push(argument);
                     }
                 } else {
-                    const attribute = new keras.Attribute(metadata.attribute(type, name), name, value);
+                    const attribute = new keras.Attribute(metadata.attribute(type, name), name, arg);
                     this._attributes.push(attribute);
                 }
             }
@@ -962,11 +966,11 @@ keras.Attribute = class {
 
 keras.Tensor = class {
 
-    constructor(name, shape, type, quantization, layout, data) {
+    constructor(name, shape, type, quantization, encoding, data) {
         this._name = name;
         this._type = new keras.TensorType(type, new keras.TensorShape(shape));
         this._quantization = quantization;
-        this._layout = layout;
+        this._encoding = encoding;
         this._data = data;
     }
 
@@ -978,8 +982,8 @@ keras.Tensor = class {
         return this._type;
     }
 
-    get layout() {
-        return this._layout;
+    get encoding() {
+        return this._encoding;
     }
 
     get quantization() {
@@ -992,7 +996,7 @@ keras.Tensor = class {
     }
 
     get values() {
-        if (this._layout === '|') {
+        if (this._encoding === '|') {
             return this._data;
         }
         if (this._data === null) {
diff --git a/source/mlir.js b/source/mlir.js
index 9aa80e82828..e04b3ab431e 100644
--- a/source/mlir.js
+++ b/source/mlir.js
@@ -403,7 +403,7 @@ mlir.Tensor = class {
         return null;
     }
 
-    get layout() {
+    get encoding() {
         switch (this._type.dataType) {
             case 'float32': return '|';
             default: return '<';
diff --git a/source/mnn.js b/source/mnn.js
index 00136016f99..24a631aa49b 100644
--- a/source/mnn.js
+++ b/source/mnn.js
@@ -364,7 +364,7 @@ mnn.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         switch (this._type.dataType) {
             case 'int32':
             case 'float32':
diff --git a/source/mslite.js b/source/mslite.js
index dd99252748f..256b4f8ef87 100644
--- a/source/mslite.js
+++ b/source/mslite.js
@@ -321,7 +321,7 @@ mslite.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         switch (this._type.dataType) {
             case 'string': return '|';
             default: return '<';
diff --git a/source/mxnet.js b/source/mxnet.js
index ef70f39c6ec..1cc0570c898 100644
--- a/source/mxnet.js
+++ b/source/mxnet.js
@@ -750,7 +750,7 @@ mxnet.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         return '<';
     }
 
diff --git a/source/nnabla.js b/source/nnabla.js
index ae32bd37b58..c44fa99ce49 100644
--- a/source/nnabla.js
+++ b/source/nnabla.js
@@ -326,7 +326,7 @@ nnabla.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         return '|';
     }
 
diff --git a/source/nnc.js b/source/nnc.js
new file mode 100644
index 00000000000..da37e7ead24
--- /dev/null
+++ b/source/nnc.js
@@ -0,0 +1,32 @@
+
+var nnc = {};
+
+nnc.ModelFactory = class {
+
+    match(context) {
+        const stream = context.stream;
+        const signature = [ 0xC0, 0x0F, 0x00, 0x00, 0x45, 0x4E, 0x4E, 0x43 ];
+        if (stream && signature.length <= stream.length && stream.peek(signature.length).every((value, index) => value === signature[index])) {
+            return 'nnc';
+        }
+        return '';
+    }
+
+    async open(/* context, target */) {
+        throw new nnc.Error('File contains undocumented NNC data.');
+    }
+};
+
+nnc.Error = class extends Error {
+
+    constructor(message) {
+        super(message);
+        this.name = 'Error loading NNC model.';
+    }
+};
+
+if (typeof module !== 'undefined' && typeof module.exports === 'object') {
+    module.exports.ModelFactory = nnc.ModelFactory;
+}
+
+
diff --git a/source/numpy.js b/source/numpy.js
index 4f1766f5225..3088abf8854 100644
--- a/source/numpy.js
+++ b/source/numpy.js
@@ -233,7 +233,7 @@ numpy.Tensor = class  {
     constructor(array) {
         this.type = new numpy.TensorType(array.dtype.__name__, new numpy.TensorShape(array.shape));
         this.values = this.type.dataType == 'string' || this.type.dataType == 'object' ? array.flatten().tolist() : array.tobytes();
-        this.layout = this.type.dataType == 'string' || this.type.dataType == 'object' ? '|' : array.dtype.byteorder;
+        this.encoding = this.type.dataType == 'string' || this.type.dataType == 'object' ? '|' : array.dtype.byteorder;
     }
 };
 
diff --git a/source/om.js b/source/om.js
index 2bfe658e240..cbd57b1d7fe 100644
--- a/source/om.js
+++ b/source/om.js
@@ -407,7 +407,7 @@ om.Container = class {
                             break;
                         }
                         default: {
-                            throw new om.Error("Unsupported partition type '" + partition.type + "'.");
+                            throw new om.Error('Unsupported DaVinci OM partition type.');
                         }
                     }
                 }
diff --git a/source/onnx-metadata.json b/source/onnx-metadata.json
index 574a83f6f42..8ba1d8d5b2e 100644
--- a/source/onnx-metadata.json
+++ b/source/onnx-metadata.json
@@ -7342,7 +7342,7 @@
       {
         "name": "input",
         "type": "T1",
-        "description": "For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimentions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order."
+        "description": "For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimensions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order."
       },
       {
         "name": "dft_length",
@@ -9094,7 +9094,7 @@
     "module": "ai.onnx",
     "version": 11,
     "support_level": "common",
-    "description": "A Function to fuse calculation for Scale, Zero Point and FP32->8Bit convertion of FP32 Input data.\nOutputs Scale, ZeroPoint and Quantized Input for a given FP32 Input.\nScale is calculated as:\n```\ny_scale = (max(x) - min(x))/(qmax - qmin)\n```\n\n* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8\n* data range is adjusted to include 0.\n\nZero point is calculated as:\n```\nintermediate_zero_point = qmin - min(x)/y_scale\ny_zero_point = cast(round(saturate(itermediate_zero_point)))\n```\n\n* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8\n* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.\n* rounding to nearest ties to even.\n\nData quantization formula is:\n```\ny = saturate (round (x / y_scale) + y_zero_point)\n```\n\n* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.\n* rounding to nearest ties to even.\n",
+    "description": "A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data.\nOutputs Scale, ZeroPoint and Quantized Input for a given FP32 Input.\nScale is calculated as:\n```\ny_scale = (max(x) - min(x))/(qmax - qmin)\n```\n\n* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8\n* data range is adjusted to include 0.\n\nZero point is calculated as:\n```\nintermediate_zero_point = qmin - min(x)/y_scale\ny_zero_point = cast(round(saturate(itermediate_zero_point)))\n```\n\n* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8\n* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.\n* rounding to nearest ties to even.\n\nData quantization formula is:\n```\ny = saturate (round (x / y_scale) + y_zero_point)\n```\n\n* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.\n* rounding to nearest ties to even.\n",
     "inputs": [
       {
         "name": "x",
@@ -9151,7 +9151,7 @@
     "module": "ai.onnx",
     "version": 12,
     "support_level": "common",
-    "description": "An einsum of the form `term1, term2 -> output-term` produces an output tensor using the following equation\n\n```\noutput[output-term] = reduce-sum( input1[term1] * input2[term] )\n```\n\nwhere the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2)\nthat do not occur in the output-term.\n\nThe Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation\nconvention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to\nan operand tensor, and the characters within the terms correspond to operands dimensions.\n\nThis sequence may be followed by \"->\" to separate the left and right hand side of the equation.\nIf the equation contains \"->\" followed by the right-hand side, the explicit (not classical) form of the Einstein\nsummation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases,\noutput indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the\nequation.\n\nWhen a dimension character is repeated in the left-hand side, it represents summation along the dimension.\n\nThe equation may contain ellipsis (\"...\") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions.\nSpecifically, every occurrence of ellipsis in the equation must represent the same number of dimensions.\nThe right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the\nbeginning of the output. The equation string may contain space (U+0020) character.\n",
+    "description": "An einsum of the form `term1, term2 -> output-term` produces an output tensor using the following equation\n\n```\noutput[output-term] = reduce-sum( input1[term1] * input2[term2] )\n```\n\nwhere the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2)\nthat do not occur in the output-term.\n\nThe Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation\nconvention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to\nan operand tensor, and the characters within the terms correspond to operands dimensions.\n\nThis sequence may be followed by \"->\" to separate the left and right hand side of the equation.\nIf the equation contains \"->\" followed by the right-hand side, the explicit (not classical) form of the Einstein\nsummation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases,\noutput indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the\nequation.\n\nWhen a dimension character is repeated in the left-hand side, it represents summation along the dimension.\n\nThe equation may contain ellipsis (\"...\") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions.\nSpecifically, every occurrence of ellipsis in the equation must represent the same number of dimensions.\nThe right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the\nbeginning of the output. The equation string may contain space (U+0020) character.\n",
     "attributes": [
       {
         "name": "equation",
@@ -10173,7 +10173,7 @@
     "module": "ai.onnx.ml",
     "version": 1,
     "support_level": "common",
-    "description": "Concatenates input tensors into one continuous output.<br>\n    All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C].\n    Inputs are copied to the output maintaining the order of the input arguments.<br>\n    All inputs must be integers or floats, while the output will be all floating point values.\n",
+    "description": "Concatenates input tensors into one continuous output.<br>\n    All input shapes are 2-D and are concatenated along the second dimension. 1-D tensors are treated as [1,C].\n    Inputs are copied to the output maintaining the order of the input arguments.<br>\n    All inputs must be integers or floats, while the output will be all floating point values.\n",
     "attributes": [
       {
         "name": "inputdimensions",
@@ -16260,6 +16260,16 @@
           "tensor(int64)"
         ]
       }
+    ],
+    "examples": [
+      {
+        "summary": "string_int_label_encoder",
+        "code": "node = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=[\"a\", \"b\", \"c\"],\n    values_int64s=[0, 1, 2],\n    default_int64=42,\n)\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, 42, 2, 42]).astype(np.int64)\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_string_int\",\n)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=[\"a\", \"b\", \"c\"],\n    values_int64s=[0, 1, 2],\n)\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, -1, 2, -1]).astype(np.int64)\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_string_int_no_default\",\n)"
+      },
+      {
+        "summary": "tensor_based_label_encoder",
+        "code": "tensor_keys = make_tensor(\n    \"keys_tensor\", onnx.TensorProto.STRING, (3,), [\"a\", \"b\", \"c\"]\n)\nrepeated_string_keys = [\"a\", \"b\", \"c\"]\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, 42, 2, 42]).astype(np.int16)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_tensor=tensor_keys,\n    values_tensor=make_tensor(\n        \"values_tensor\", onnx.TensorProto.INT16, (3,), [0, 1, 2]\n    ),\n    default_tensor=make_tensor(\n        \"default_tensor\", onnx.TensorProto.INT16, (1,), [42]\n    ),\n)\n\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_tensor_mapping\",\n)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=repeated_string_keys,\n    values_tensor=make_tensor(\n        \"values_tensor\", onnx.TensorProto.INT16, (3,), [0, 1, 2]\n    ),\n    default_tensor=make_tensor(\n        \"default_tensor\", onnx.TensorProto.INT16, (1,), [42]\n    ),\n)\n\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_tensor_value_only_mapping\",\n)"
+      }
     ]
   },
   {
@@ -16363,6 +16373,153 @@
           "tensor(float)"
         ]
       }
+    ],
+    "examples": [
+      {
+        "summary": "string_int_label_encoder",
+        "code": "node = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=[\"a\", \"b\", \"c\"],\n    values_int64s=[0, 1, 2],\n    default_int64=42,\n)\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, 42, 2, 42]).astype(np.int64)\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_string_int\",\n)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=[\"a\", \"b\", \"c\"],\n    values_int64s=[0, 1, 2],\n)\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, -1, 2, -1]).astype(np.int64)\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_string_int_no_default\",\n)"
+      },
+      {
+        "summary": "tensor_based_label_encoder",
+        "code": "tensor_keys = make_tensor(\n    \"keys_tensor\", onnx.TensorProto.STRING, (3,), [\"a\", \"b\", \"c\"]\n)\nrepeated_string_keys = [\"a\", \"b\", \"c\"]\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, 42, 2, 42]).astype(np.int16)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_tensor=tensor_keys,\n    values_tensor=make_tensor(\n        \"values_tensor\", onnx.TensorProto.INT16, (3,), [0, 1, 2]\n    ),\n    default_tensor=make_tensor(\n        \"default_tensor\", onnx.TensorProto.INT16, (1,), [42]\n    ),\n)\n\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_tensor_mapping\",\n)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=repeated_string_keys,\n    values_tensor=make_tensor(\n        \"values_tensor\", onnx.TensorProto.INT16, (3,), [0, 1, 2]\n    ),\n    default_tensor=make_tensor(\n        \"default_tensor\", onnx.TensorProto.INT16, (1,), [42]\n    ),\n)\n\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_tensor_value_only_mapping\",\n)"
+      }
+    ]
+  },
+  {
+    "name": "LabelEncoder",
+    "module": "ai.onnx.ml",
+    "version": 4,
+    "support_level": "common",
+    "description": "Maps each element in the input tensor to another value.<br>\n    The mapping is determined by the two parallel attributes, 'keys_*' and\n    'values_*' attribute. The i-th value in the specified 'keys_*' attribute\n    would be mapped to the i-th value in the specified 'values_*' attribute. It\n    implies that input's element type and the element type of the specified\n    'keys_*' should be identical while the output type is identical to the\n    specified 'values_*' attribute. Note that the 'keys_*' and 'values_*' attributes\n    must have the same length. If an input element can not be found in the\n    specified 'keys_*' attribute, the 'default_*' that matches the specified\n    'values_*' attribute may be used as its output value. The type of the 'default_*'\n    attribute must match the 'values_*' attribute chosen. <br>\n    Let's consider an example which maps a string tensor to an integer tensor.\n    Assume and 'keys_strings' is [\"Amy\", \"Sally\"], 'values_int64s' is [5, 6],\n    and 'default_int64' is '-1'.  The input [\"Dori\", \"Amy\", \"Amy\", \"Sally\",\n    \"Sally\"] would be mapped to [-1, 5, 5, 6, 6].<br>\n    Since this operator is an one-to-one mapping, its input and output shapes\n    are the same. Notice that only one of 'keys_*'/'values_*' can be set.<br>\n    Float keys with value 'NaN' match any input 'NaN' value regardless of bit\n    value. If a key is repeated, the last key takes precedence.\n",
+    "attributes": [
+      {
+        "name": "default_float",
+        "type": "float32",
+        "required": false,
+        "description": "A float."
+      },
+      {
+        "name": "default_int64",
+        "type": "int64",
+        "required": false,
+        "default": -1,
+        "description": "An integer."
+      },
+      {
+        "name": "default_string",
+        "type": "string",
+        "required": false,
+        "default": "_Unused",
+        "description": "A string."
+      },
+      {
+        "name": "default_tensor",
+        "type": "tensor",
+        "required": false,
+        "description": "A default tensor."
+      },
+      {
+        "name": "keys_floats",
+        "type": "float32[]",
+        "required": false,
+        "description": "A list of floats."
+      },
+      {
+        "name": "keys_int64s",
+        "type": "int64[]",
+        "required": false,
+        "description": "A list of ints."
+      },
+      {
+        "name": "keys_strings",
+        "type": "string[]",
+        "required": false,
+        "description": "A list of strings."
+      },
+      {
+        "name": "keys_tensor",
+        "type": "tensor",
+        "required": false,
+        "description": "Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set."
+      },
+      {
+        "name": "values_floats",
+        "type": "float32[]",
+        "required": false,
+        "description": "A list of floats."
+      },
+      {
+        "name": "values_int64s",
+        "type": "int64[]",
+        "required": false,
+        "description": "A list of ints."
+      },
+      {
+        "name": "values_strings",
+        "type": "string[]",
+        "required": false,
+        "description": "A list of strings."
+      },
+      {
+        "name": "values_tensor",
+        "type": "tensor",
+        "required": false,
+        "description": "Values encoded as a 1D tensor. One and only one of 'values_*'s should be set."
+      }
+    ],
+    "inputs": [
+      {
+        "name": "X",
+        "type": "T1",
+        "description": "Input data. It must have the same element type as the keys_* attribute set."
+      }
+    ],
+    "min_input": 1,
+    "max_input": 1,
+    "outputs": [
+      {
+        "name": "Y",
+        "type": "T2",
+        "description": "Output data. This tensor's element type is based on the values_* attribute set."
+      }
+    ],
+    "min_output": 1,
+    "max_output": 1,
+    "type_constraints": [
+      {
+        "description": "The input type is a tensor of any shape.",
+        "type_param_str": "T1",
+        "allowed_type_strs": [
+          "tensor(string)",
+          "tensor(int64)",
+          "tensor(float)",
+          "tensor(int32)",
+          "tensor(int16)",
+          "tensor(double)"
+        ]
+      },
+      {
+        "description": "Output type is determined by the specified 'values_*' attribute.",
+        "type_param_str": "T2",
+        "allowed_type_strs": [
+          "tensor(string)",
+          "tensor(int64)",
+          "tensor(float)",
+          "tensor(int32)",
+          "tensor(int16)",
+          "tensor(double)"
+        ]
+      }
+    ],
+    "examples": [
+      {
+        "summary": "string_int_label_encoder",
+        "code": "node = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=[\"a\", \"b\", \"c\"],\n    values_int64s=[0, 1, 2],\n    default_int64=42,\n)\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, 42, 2, 42]).astype(np.int64)\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_string_int\",\n)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=[\"a\", \"b\", \"c\"],\n    values_int64s=[0, 1, 2],\n)\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, -1, 2, -1]).astype(np.int64)\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_string_int_no_default\",\n)"
+      },
+      {
+        "summary": "tensor_based_label_encoder",
+        "code": "tensor_keys = make_tensor(\n    \"keys_tensor\", onnx.TensorProto.STRING, (3,), [\"a\", \"b\", \"c\"]\n)\nrepeated_string_keys = [\"a\", \"b\", \"c\"]\nx = np.array([\"a\", \"b\", \"d\", \"c\", \"g\"]).astype(object)\ny = np.array([0, 1, 42, 2, 42]).astype(np.int16)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_tensor=tensor_keys,\n    values_tensor=make_tensor(\n        \"values_tensor\", onnx.TensorProto.INT16, (3,), [0, 1, 2]\n    ),\n    default_tensor=make_tensor(\n        \"default_tensor\", onnx.TensorProto.INT16, (1,), [42]\n    ),\n)\n\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_tensor_mapping\",\n)\n\nnode = onnx.helper.make_node(\n    \"LabelEncoder\",\n    inputs=[\"X\"],\n    outputs=[\"Y\"],\n    domain=\"ai.onnx.ml\",\n    keys_strings=repeated_string_keys,\n    values_tensor=make_tensor(\n        \"values_tensor\", onnx.TensorProto.INT16, (3,), [0, 1, 2]\n    ),\n    default_tensor=make_tensor(\n        \"default_tensor\", onnx.TensorProto.INT16, (1,), [42]\n    ),\n)\n\nexpect(\n    node,\n    inputs=[x],\n    outputs=[y],\n    name=\"test_ai_onnx_ml_label_encoder_tensor_value_only_mapping\",\n)"
+      }
     ]
   },
   {
@@ -19986,7 +20143,7 @@
     "module": "ai.onnx",
     "version": 9,
     "support_level": "common",
-    "description": "MaxUnpool essentially computes the partial inverse of the MaxPool op.\n The input information to this op is typically the output information from a MaxPool op. The first\n input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)\n from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding\n to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.\n The third (optional) input is a tensor that specifies the output size of the unpooling operation.\n\nMaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal\n values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling\n the result of an unpooling operation should give back the original input to the unpooling op.\n\nMaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.\n The third input argument, output_size, is meant to disambiguate the op and produce output tensor of\n known/predictable size.\n\nIn addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,\n which define the exact unpooling op. The attributes typically have the same values as the corrsponding\n pooling op that the unpooling op is trying to invert.\n",
+    "description": "MaxUnpool essentially computes the partial inverse of the MaxPool op.\n The input information to this op is typically the output information from a MaxPool op. The first\n input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)\n from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corresponding\n to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.\n The third (optional) input is a tensor that specifies the output size of the unpooling operation.\n\nMaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal\n values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling\n the result of an unpooling operation should give back the original input to the unpooling op.\n\nMaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.\n The third input argument, output_size, is meant to disambiguate the op and produce output tensor of\n known/predictable size.\n\nIn addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,\n which define the exact unpooling op. The attributes typically have the same values as the corresponding\n pooling op that the unpooling op is trying to invert.\n",
     "attributes": [
       {
         "name": "kernel_shape",
@@ -20071,7 +20228,7 @@
     "module": "ai.onnx",
     "version": 11,
     "support_level": "common",
-    "description": "MaxUnpool essentially computes the partial inverse of the MaxPool op.\n The input information to this op is typically the output information from a MaxPool op. The first\n input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)\n from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding\n to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.\n The third (optional) input is a tensor that specifies the output size of the unpooling operation.\n\nMaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal\n values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling\n the result of an unpooling operation should give back the original input to the unpooling op.\n\nMaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.\n The third input argument, output_size, is meant to disambiguate the op and produce output tensor of\n known/predictable size.\n\nIn addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,\n which define the exact unpooling op. The attributes typically have the same values as the corrsponding\n pooling op that the unpooling op is trying to invert.\n",
+    "description": "MaxUnpool essentially computes the partial inverse of the MaxPool op.\n The input information to this op is typically the output information from a MaxPool op. The first\n input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)\n from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corresponding\n to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.\n The third (optional) input is a tensor that specifies the output size of the unpooling operation.\n\nMaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal\n values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling\n the result of an unpooling operation should give back the original input to the unpooling op.\n\nMaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.\n The third input argument, output_size, is meant to disambiguate the op and produce output tensor of\n known/predictable size.\n\nIn addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,\n which define the exact unpooling op. The attributes typically have the same values as the corresponding\n pooling op that the unpooling op is trying to invert.\n",
     "attributes": [
       {
         "name": "kernel_shape",
@@ -20347,7 +20504,7 @@
         "name": "axes",
         "type": "int64[]",
         "required": false,
-        "description": "A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance."
+        "description": "A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance."
       }
     ],
     "inputs": [
@@ -20397,7 +20554,7 @@
         "name": "axes",
         "type": "int64[]",
         "required": false,
-        "description": "A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance."
+        "description": "A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance."
       }
     ],
     "inputs": [
@@ -26040,7 +26197,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the L1 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the L1 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -26076,7 +26233,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -26114,7 +26271,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the L1 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the L1 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -26157,7 +26314,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -26341,7 +26498,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the L2 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the L2 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -26377,7 +26534,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -26415,7 +26572,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the L2 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the L2 norm of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -26458,7 +26615,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -26634,7 +26791,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the log sum of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the log sum of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -26670,7 +26827,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -26704,7 +26861,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the log sum of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the log sum of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -26747,7 +26904,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -26927,7 +27084,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the log sum exponent of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the log sum exponent of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -26963,7 +27120,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -27001,7 +27158,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the log sum exponent of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the log sum exponent of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -27044,7 +27201,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -27132,21 +27289,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[True],\n#  [True],\n#  [True],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27205,21 +27366,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[True],\n#  [True],\n#  [True],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27280,21 +27445,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[True],\n#  [True],\n#  [True],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27303,7 +27472,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the max of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the max of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -27339,7 +27508,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision and 8 bit numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -27356,21 +27525,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[True],\n#  [True],\n#  [True],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27379,7 +27552,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the max of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the max of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -27422,7 +27595,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision and 8 bit numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -27439,21 +27612,113 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[True],\n#  [True],\n#  [True],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_bool_inputs\",\n)"
+      },
+      {
+        "summary": "default_axes_keepdims",
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      },
+      {
+        "summary": "do_not_keepdims",
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      },
+      {
+        "summary": "keepdims",
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      },
+      {
+        "summary": "negative_axes_keepdims",
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      }
+    ]
+  },
+  {
+    "name": "ReduceMax",
+    "module": "ai.onnx",
+    "version": 20,
+    "support_level": "common",
+    "description": "Computes the max of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nIf the input data type is Boolean, the comparison should consider `False < True`.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
+    "attributes": [
+      {
+        "name": "keepdims",
+        "type": "int64",
+        "required": false,
+        "default": 1,
+        "description": "Keep the reduced dimension or not, default 1 means keep reduced dimension."
+      },
+      {
+        "name": "noop_with_empty_axes",
+        "type": "int64",
+        "required": false,
+        "description": "Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor."
+      }
+    ],
+    "inputs": [
+      {
+        "name": "data",
+        "type": "T",
+        "description": "An input tensor."
+      },
+      {
+        "name": "axes",
+        "type": "tensor(int64)",
+        "option": "optional",
+        "description": "Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data)."
+      }
+    ],
+    "min_input": 1,
+    "max_input": 2,
+    "outputs": [
+      {
+        "name": "reduced",
+        "type": "T",
+        "description": "Reduced output tensor."
+      }
+    ],
+    "min_output": 1,
+    "max_output": 1,
+    "inputs_range": "1 - 2",
+    "type_constraints": [
+      {
+        "description": "Constrain input and output types to numeric and Boolean tensors.",
+        "type_param_str": "T",
+        "allowed_type_strs": [
+          "tensor(uint32)",
+          "tensor(uint64)",
+          "tensor(int32)",
+          "tensor(int64)",
+          "tensor(float16)",
+          "tensor(float)",
+          "tensor(double)",
+          "tensor(bfloat16)",
+          "tensor(uint8)",
+          "tensor(int8)",
+          "tensor(bool)"
+        ]
+      }
+    ],
+    "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[True],\n#  [True],\n#  [True],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\nnode = onnx.helper.make_node(\n    \"ReduceMax\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdim_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_max_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[20., 2.]\n# [40., 2.]\n# [60., 2.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMax\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[20., 2.]]\n# [[40., 2.]]\n# [[60., 2.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_max_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27608,7 +27873,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the mean of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the mean of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -27644,7 +27909,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -27682,7 +27947,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the mean of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the mean of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -27725,7 +27990,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -27813,21 +28078,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[ True],\n#  [False],\n#  [False],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27886,21 +28155,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[ True],\n#  [False],\n#  [False],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27961,21 +28234,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[ True],\n#  [False],\n#  [False],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -27984,7 +28261,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the min of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the min of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -28020,7 +28297,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision and 8 bit numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -28037,21 +28314,25 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[ True],\n#  [False],\n#  [False],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -28060,7 +28341,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the min of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the min of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -28103,7 +28384,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision and 8 bit numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -28120,21 +28401,113 @@
       }
     ],
     "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[ True],\n#  [False],\n#  [False],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_bool_inputs\",\n)"
+      },
+      {
+        "summary": "default_axes_keepdims",
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      },
+      {
+        "summary": "do_not_keepdims",
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      },
+      {
+        "summary": "keepdims",
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      },
+      {
+        "summary": "negative_axes_keepdims",
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
+      }
+    ]
+  },
+  {
+    "name": "ReduceMin",
+    "module": "ai.onnx",
+    "version": 20,
+    "support_level": "common",
+    "description": "Computes the min of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nIf the input data type is Boolean, the comparison should consider `False < True`.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
+    "attributes": [
+      {
+        "name": "keepdims",
+        "type": "int64",
+        "required": false,
+        "default": 1,
+        "description": "Keep the reduced dimension or not, default 1 means keep reduced dimension."
+      },
+      {
+        "name": "noop_with_empty_axes",
+        "type": "int64",
+        "required": false,
+        "description": "Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor."
+      }
+    ],
+    "inputs": [
+      {
+        "name": "data",
+        "type": "T",
+        "description": "An input tensor."
+      },
+      {
+        "name": "axes",
+        "type": "tensor(int64)",
+        "option": "optional",
+        "description": "Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data)."
+      }
+    ],
+    "min_input": 1,
+    "max_input": 2,
+    "outputs": [
+      {
+        "name": "reduced",
+        "type": "T",
+        "description": "Reduced output tensor."
+      }
+    ],
+    "min_output": 1,
+    "max_output": 1,
+    "inputs_range": "1 - 2",
+    "type_constraints": [
+      {
+        "description": "Constrain input and output types to numeric and Boolean tensors.",
+        "type_param_str": "T",
+        "allowed_type_strs": [
+          "tensor(uint32)",
+          "tensor(uint64)",
+          "tensor(int32)",
+          "tensor(int64)",
+          "tensor(float16)",
+          "tensor(float)",
+          "tensor(double)",
+          "tensor(bfloat16)",
+          "tensor(uint8)",
+          "tensor(int8)",
+          "tensor(bool)"
+        ]
+      }
+    ],
+    "examples": [
+      {
+        "summary": "bool_inputs",
+        "code": "axes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[True, True], [True, False], [False, True], [False, False]],\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims))\n# print(reduced)\n# [[ True],\n#  [False],\n#  [False],\n#  [False]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_bool_inputs\",\n)"
+      },
       {
         "summary": "default_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = None\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\", inputs=[\"data\"], outputs=[\"reduced\"], keepdims=keepdims\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n# print(reduced)\n# [[[1.]]]\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data],\n    outputs=[reduced],\n    name=\"test_reduce_min_default_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "do_not_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 0\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[5., 1.]\n# [30., 1.]\n# [55., 1.]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_do_not_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([1], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       },
       {
         "summary": "negative_axes_keepdims",
-        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n)"
+        "code": "shape = [3, 2, 2]\naxes = np.array([-2], dtype=np.int64)\nkeepdims = 1\n\nnode = onnx.helper.make_node(\n    \"ReduceMin\",\n    inputs=[\"data\", \"axes\"],\n    outputs=[\"reduced\"],\n    keepdims=keepdims,\n)\n\ndata = np.array(\n    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],\n    dtype=np.float32,\n)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n# print(reduced)\n# [[[5., 1.]]\n# [[30., 1.]]\n# [[55., 1.]]]\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_example\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)\n\nnp.random.seed(0)\ndata = np.random.uniform(-10, 10, shape).astype(np.float32)\nreduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)\n\nexpect(\n    node,\n    inputs=[data, axes],\n    outputs=[reduced],\n    name=\"test_reduce_min_negative_axes_keepdims_random\",\n    opset_imports=[onnx.helper.make_opsetid(\"\", 18)],\n)"
       }
     ]
   },
@@ -28289,7 +28662,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the product of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the product of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -28325,7 +28698,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -28363,7 +28736,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the product of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the product of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -28406,7 +28779,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -28598,7 +28971,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the sum of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the sum of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -28641,7 +29014,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -28829,7 +29202,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Computes the sum square of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the sum square of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "axes",
@@ -28865,7 +29238,7 @@
     "max_output": 1,
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -28903,7 +29276,7 @@
     "module": "ai.onnx",
     "version": 18,
     "support_level": "common",
-    "description": "Computes the sum square of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults keepdims to\nFalse instead of True.",
+    "description": "Computes the sum square of the input tensor's elements along the provided axes. The resulting\ntensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then\nthe resulting tensor has the reduced dimension pruned. Input tensors of rank zero are\nvalid.\n\nThe above behavior is similar to numpy, with the exception that numpy defaults `keepdims`\nto `False` instead of `True`.",
     "attributes": [
       {
         "name": "keepdims",
@@ -28946,7 +29319,7 @@
     "inputs_range": "1 - 2",
     "type_constraints": [
       {
-        "description": "Constrain input and output types to high-precision numeric tensors.",
+        "description": "Constrain input and output types to numeric tensors.",
         "type_param_str": "T",
         "allowed_type_strs": [
           "tensor(uint32)",
@@ -31227,7 +31600,7 @@
     "module": "ai.onnx",
     "version": 11,
     "support_level": "common",
-    "description": "Round takes one input Tensor and rounds the values, element-wise, meaning\nit finds the nearest integer for each value.\nIn case of halfs, the rule is to round them to the nearest even integer.\nIf input x is integral, +0, -0, NaN,  or infinite, x itself is returned.\nThe output tensor has the same shape and type as the input.\n\nExamples:\n```\nround([0.9]) = [1.0]\nround([2.5]) = [2.0]\nround([2.3]) = [2.0]\nround([1.5]) = [2.0]\nround([-4.5]) = [-4.0]\n```\n",
+    "description": "Round takes one input Tensor and rounds the values, element-wise, meaning\nit finds the nearest integer for each value.\nIn case of halves, the rule is to round them to the nearest even integer.\nIf input x is integral, +0, -0, NaN,  or infinite, x itself is returned.\nThe output tensor has the same shape and type as the input.\n\nExamples:\n```\nround([0.9]) = [1.0]\nround([2.5]) = [2.0]\nround([2.3]) = [2.0]\nround([1.5]) = [2.0]\nround([-4.5]) = [-4.0]\n```\n",
     "inputs": [
       {
         "name": "X",
@@ -34899,7 +35272,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Produces a slice of the input tensor along multiple axes. Similar to numpy:\nhttps://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding\n\nSlice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor\nof its input `data` tensor.\n\nAn effective `starts[i]`, `ends[i]`, and `steps[i]` must be computed for each `i`\nin `[0, ... r-1]` where `r = rank(input)` as follows:\n\nIf `axes` are omitted, they are set to `[0, ..., r-1]`.\nIf `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`\n\nThe effective values are initialized as `start[i] = 0`, `ends[i] = dims[i]` where\n`dims` are the dimensions of `input` and `steps[i] = `1.\n\nAll negative elements of `axes` are made non-negatve by adding `r` to them, where\n`r =rank(input)`.\n\nAll negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them,\nwhere `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted\n`starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping\nand `[0, dims[axes[i]]-1]` for negative stepping.\n\nThe clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must\naccommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping\n`ends[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it\nis clamped to `[-1, dims[axes[i]]-1]`.\n\nFinally, `steps[axes[i]] = steps[i]`.\n\nFor slicing to the end of a dimension with unknown size, it is recommended to pass\nin `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward.\n\nExample 1:\n\n```\ndata = [\n    [1, 2, 3, 4],\n    [5, 6, 7, 8],\n]\naxes = [0, 1]\nstarts = [1, 0]\nends = [2, 3]\nsteps = [1, 2]\nresult = [\n    [5, 7],\n]\n```\n\nExample 2:\n\n```\ndata = [\n    [1, 2, 3, 4],\n    [5, 6, 7, 8],\n]\nstarts = [0, 1]\nends = [-1, 1000]\nresult = [\n    [2, 3, 4],\n]\n```\n",
+    "description": "Produces a slice of the input tensor along multiple axes. Similar to numpy:\nhttps://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding\n\nSlice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor\nof its input `data` tensor.\n\nAn effective `starts[i]`, `ends[i]`, and `steps[i]` must be computed for each `i`\nin `[0, ... r-1]` where `r = rank(input)` as follows:\n\nIf `axes` are omitted, they are set to `[0, ..., r-1]`.\nIf `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`\n\nThe effective values are initialized as `start[i] = 0`, `ends[i] = dims[i]` where\n`dims` are the dimensions of `input` and `steps[i] = 1`.\n\nAll negative elements of `axes` are made non-negative by adding `r` to them, where\n`r =rank(input)`.\n\nAll negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them,\nwhere `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted\n`starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping\nand `[0, dims[axes[i]]-1]` for negative stepping.\n\nThe clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must\naccommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping\n`ends[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it\nis clamped to `[-1, dims[axes[i]]-1]`.\n\nFinally, `steps[axes[i]] = steps[i]`.\n\nFor slicing to the end of a dimension with unknown size, it is recommended to pass\nin `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward.\n\nExample 1:\n\n```\ndata = [\n    [1, 2, 3, 4],\n    [5, 6, 7, 8],\n]\naxes = [0, 1]\nstarts = [1, 0]\nends = [2, 3]\nsteps = [1, 2]\nresult = [\n    [5, 7],\n]\n```\n\nExample 2:\n\n```\ndata = [\n    [1, 2, 3, 4],\n    [5, 6, 7, 8],\n]\nstarts = [0, 1]\nends = [-1, 1000]\nresult = [\n    [2, 3, 4],\n]\n```\n",
     "inputs": [
       {
         "name": "data",
@@ -35183,7 +35556,7 @@
     "module": "ai.onnx",
     "version": 12,
     "support_level": "common",
-    "description": "Loss function that measures the softmax cross entropy\nbetween 'scores' and 'labels'.\nThis operator first computes a loss tensor whose shape is identical to the labels input.\nIf the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).\nIf the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),\nthe loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.\nAfter L is available, this operator can optionally do a reduction operator.\n\nshape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),\n        with K >= 1 in case of K-dimensional loss.\nshape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),\n        with K >= 1 in case of K-dimensional loss.\n\nThe loss for one sample, l_i, can caculated as follows:\n    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.\nor\n    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.\n\nloss is zero for the case when label-value equals ignore_index.\n    l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index\n\nwhere:\n    p = Softmax(scores)\n    y = Log(p)\n    c = labels[i][d1][d2]...[dk]\n\nFinally, L is optionally reduced:\nIf reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).\nIf reduction = 'sum', the output is scalar: Sum(L).\nIf reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),\nwhere tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].\n",
+    "description": "Loss function that measures the softmax cross entropy\nbetween 'scores' and 'labels'.\nThis operator first computes a loss tensor whose shape is identical to the labels input.\nIf the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).\nIf the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),\nthe loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.\nAfter L is available, this operator can optionally do a reduction operator.\n\nshape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),\n        with K >= 1 in case of K-dimensional loss.\nshape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),\n        with K >= 1 in case of K-dimensional loss.\n\nThe loss for one sample, l_i, can calculated as follows:\n    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.\nor\n    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.\n\nloss is zero for the case when label-value equals ignore_index.\n    l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index\n\nwhere:\n    p = Softmax(scores)\n    y = Log(p)\n    c = labels[i][d1][d2]...[dk]\n\nFinally, L is optionally reduced:\nIf reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).\nIf reduction = 'sum', the output is scalar: Sum(L).\nIf reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),\nwhere tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].\n",
     "attributes": [
       {
         "name": "ignore_index",
@@ -35399,7 +35772,7 @@
     "module": "ai.onnx",
     "version": 13,
     "support_level": "common",
-    "description": "Loss function that measures the softmax cross entropy\nbetween 'scores' and 'labels'.\nThis operator first computes a loss tensor whose shape is identical to the labels input.\nIf the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).\nIf the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),\nthe loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.\nAfter L is available, this operator can optionally do a reduction operator.\n\n* shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),\n  with K >= 1 in case of K-dimensional loss.\n* shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),\n  with K >= 1 in case of K-dimensional loss.\n\nThe loss for one sample, l_i, can caculated as follows:\n```\nl[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.\n```\nor\n```\nl[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.\n```\n\nloss is zero for the case when label-value equals ignore_index.\n```\nl[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index\n```\n\nwhere:\n```\np = Softmax(scores)\ny = Log(p)\nc = labels[i][d1][d2]...[dk]\n```\n\nFinally, L is optionally reduced:\n\n* If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).\n* If reduction = 'sum', the output is scalar: Sum(L).\n* If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: `ReduceSum(L) / ReduceSum(W)`,\n  where tensor W is of shape `(N, D1, D2, ..., Dk)` and `W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]`.\n",
+    "description": "Loss function that measures the softmax cross entropy\nbetween 'scores' and 'labels'.\nThis operator first computes a loss tensor whose shape is identical to the labels input.\nIf the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).\nIf the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),\nthe loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.\nAfter L is available, this operator can optionally do a reduction operator.\n\n* shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),\n  with K >= 1 in case of K-dimensional loss.\n* shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),\n  with K >= 1 in case of K-dimensional loss.\n\nThe loss for one sample, l_i, can calculated as follows:\n```\nl[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.\n```\nor\n```\nl[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.\n```\n\nloss is zero for the case when label-value equals ignore_index.\n```\nl[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index\n```\n\nwhere:\n```\np = Softmax(scores)\ny = Log(p)\nc = labels[i][d1][d2]...[dk]\n```\n\nFinally, L is optionally reduced:\n\n* If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).\n* If reduction = 'sum', the output is scalar: Sum(L).\n* If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: `ReduceSum(L) / ReduceSum(W)`,\n  where tensor W is of shape `(N, D1, D2, ..., Dk)` and `W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]`.\n",
     "attributes": [
       {
         "name": "ignore_index",
@@ -38556,7 +38929,7 @@
         "name": "nodes_missing_value_tracks_true",
         "type": "int64[]",
         "required": false,
-        "description": "For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes."
+        "description": "For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the default value is false (0) for all nodes."
       },
       {
         "name": "nodes_modes",
@@ -38729,7 +39102,7 @@
         "name": "nodes_missing_value_tracks_true",
         "type": "int64[]",
         "required": false,
-        "description": "For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes."
+        "description": "For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the default value is false (0) for all nodes."
       },
       {
         "name": "nodes_modes",
@@ -38867,7 +39240,7 @@
         "name": "nodes_missing_value_tracks_true",
         "type": "int64[]",
         "required": false,
-        "description": "For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes."
+        "description": "For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the default value is false (0) for all nodes."
       },
       {
         "name": "nodes_modes",
@@ -39022,7 +39395,7 @@
         "name": "nodes_missing_value_tracks_true",
         "type": "int64[]",
         "required": false,
-        "description": "For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes."
+        "description": "For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the default value is false (0) for all nodes."
       },
       {
         "name": "nodes_modes",
@@ -39273,7 +39646,7 @@
     "module": "ai.onnx",
     "version": 11,
     "support_level": "common",
-    "description": "Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned.\nOtherwise the input tensor is flattened and unique values of the flattened tensor are returned.\n\nThis operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs.\nThe first output tensor 'Y' contains all unique values or subtensors of the input.\nThe second optional output tensor 'indices' contains indices of 'Y' elements' first occurance in 'X'..\nThe third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. \".\nThe fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input.\n\nOutputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input.\n\nhttps://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html\n\nExample 1:\n```\ninput_X = [2, 1, 1, 3, 4, 3]\nattribute_sorted = 0\nattribute_axis = None\noutput_Y = [2, 1, 3, 4]\noutput_indices = [0, 1, 3, 4]\noutput_inverse_indices = [0, 1, 1, 2, 3, 2]\noutput_counts = [1, 2, 2, 1]\n```\n\nExample 2:\n```\ninput_X = [[1, 3], [2, 3]]\nattribute_sorted = 1\nattribute_axis = None\noutput_Y = [1, 2, 3]\noutput_indices = [0, 2, 1]\noutput_inverse_indices = [0, 2, 1, 2]\noutput_counts = [1, 1, 2]\n```\n\nExample 3:\n```\ninput_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]]\nattribute_sorted = 1\nattribute_axis = 0\noutput_Y = [[1, 0, 0], [2, 3, 4]]\noutput_indices = [0, 2]\noutput_inverse_indices = [0, 0, 1]\noutput_counts = [2, 1]\n```\n\nExample 4:\n```\ninput_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],\n            [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]\nattribute_sorted = 1\nattribute_axis = 1\n```\n\nintermediate data are presented below for better understanding:\nthere are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):\n```\nA: [[1, 1], [1, 1]],\n   [[0, 1], [0, 1]],\n   [[2, 1], [2, 1]],\n   [[0, 1], [0, 1]].\n```\n\nthere are 3 unique subtensors:\n```\n[[1, 1], [1, 1]],\n[[0, 1], [0, 1]],\n[[2, 1], [2, 1]].\n```\n\nsorted unique subtensors:\n```\nB: [[0, 1], [0, 1]],\n   [[1, 1], [1, 1]],\n   [[2, 1], [2, 1]].\n```\n\noutput_Y is constructed from B:\n```\n[[[0. 1.], [1. 1.], [2. 1.]],\n [[0. 1.], [1. 1.], [2. 1.]]]\n```\n\noutput_indices is to map from B to A:\n```\n[1, 0, 2]\n```\n\noutput_inverse_indices is to map from A to B:\n```\n[1, 0, 2, 0]\n```\n\noutput_counts:\n```\n[2, 1, 1]\n```\n",
+    "description": "Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned.\nOtherwise the input tensor is flattened and unique values of the flattened tensor are returned.\n\nThis operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs.\nThe first output tensor 'Y' contains all unique values or subtensors of the input.\nThe second optional output tensor 'indices' contains indices of 'Y' elements' first occurrence in 'X'.\nThe third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'.\nThe fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input.\n\nOutputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input.\n\nhttps://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html\n\nExample 1:\n```\ninput_X = [2, 1, 1, 3, 4, 3]\nattribute_sorted = 0\nattribute_axis = None\noutput_Y = [2, 1, 3, 4]\noutput_indices = [0, 1, 3, 4]\noutput_inverse_indices = [0, 1, 1, 2, 3, 2]\noutput_counts = [1, 2, 2, 1]\n```\n\nExample 2:\n```\ninput_X = [[1, 3], [2, 3]]\nattribute_sorted = 1\nattribute_axis = None\noutput_Y = [1, 2, 3]\noutput_indices = [0, 2, 1]\noutput_inverse_indices = [0, 2, 1, 2]\noutput_counts = [1, 1, 2]\n```\n\nExample 3:\n```\ninput_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]]\nattribute_sorted = 1\nattribute_axis = 0\noutput_Y = [[1, 0, 0], [2, 3, 4]]\noutput_indices = [0, 2]\noutput_inverse_indices = [0, 0, 1]\noutput_counts = [2, 1]\n```\n\nExample 4:\n```\ninput_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],\n            [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]\nattribute_sorted = 1\nattribute_axis = 1\n```\n\nintermediate data are presented below for better understanding:\nthere are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):\n```\nA: [[1, 1], [1, 1]],\n   [[0, 1], [0, 1]],\n   [[2, 1], [2, 1]],\n   [[0, 1], [0, 1]].\n```\n\nthere are 3 unique subtensors:\n```\n[[1, 1], [1, 1]],\n[[0, 1], [0, 1]],\n[[2, 1], [2, 1]].\n```\n\nsorted unique subtensors:\n```\nB: [[0, 1], [0, 1]],\n   [[1, 1], [1, 1]],\n   [[2, 1], [2, 1]].\n```\n\noutput_Y is constructed from B:\n```\n[[[0. 1.], [1. 1.], [2. 1.]],\n [[0. 1.], [1. 1.], [2. 1.]]]\n```\n\noutput_indices is to map from B to A:\n```\n[1, 0, 2]\n```\n\noutput_inverse_indices is to map from A to B:\n```\n[1, 0, 2, 0]\n```\n\noutput_counts:\n```\n[2, 1, 1]\n```\n",
     "attributes": [
       {
         "name": "axis",
@@ -39308,7 +39681,7 @@
         "name": "indices",
         "type": "tensor(int64)",
         "option": "optional",
-        "description": "A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. "
+        "description": "A 1-D INT64 tensor containing indices of 'Y' elements' first occurrence in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. "
       },
       {
         "name": "inverse_indices",
diff --git a/source/onnx.js b/source/onnx.js
index 4872cdb0e8f..88f7224ec4f 100644
--- a/source/onnx.js
+++ b/source/onnx.js
@@ -616,14 +616,13 @@ onnx.Tensor = class {
         this._category = category || null;
         if (tensor.indices && tensor.values) {
             this._name = tensor.values.name || '';
-            this._type = context.createTensorType(tensor.values.data_type, tensor.dims.map((dim) => dim), null);
+            this._type = context.createTensorType(tensor.values.data_type, tensor.dims.map((dim) => dim), 'sparse');
             this._location = context.createLocation(tensor.values.data_location);
-            this._layout = 'sparse';
             this._values = new onnx.Tensor(context, tensor.values);
             this._indices = new onnx.Tensor(context, tensor.indices);
         } else {
             this._name = tensor.name || '';
-            this._type = context.createTensorType(tensor.data_type, tensor.dims.map((dim) => dim), null);
+            this._type = context.createTensorType(tensor.data_type, tensor.dims.map((dim) => dim));
             this._location = context.createLocation(tensor.data_location);
             switch (tensor.data_location) {
                 case onnx.DataLocation.DEFAULT: {
@@ -633,11 +632,11 @@ onnx.Tensor = class {
                         }
                         case onnx.DataType.FLOAT:
                             this._data = new Float32Array(tensor.float_data);
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.DOUBLE:
                             this._data = new Float64Array(tensor.double_data);
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.BOOL:
                             if (tensor.int32_data && tensor.int32_data.length > 0) {
@@ -646,41 +645,41 @@ onnx.Tensor = class {
                                 for (let i = 0; i < this._data.length; i++) {
                                     this._data[i] = array[i] === 0 ? false : true;
                                 }
-                                this._layout = '|';
+                                this._encoding = '|';
                             }
                             break;
                         case onnx.DataType.INT8:
                             this._data = new Int8Array(tensor.int32_data);
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.UINT8:
                             this._data = new Uint8Array(tensor.int32_data);
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.INT16:
                             this._data = new Int32Array(tensor.int32_data);
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.UINT16:
                             this._data = new Int32Array(tensor.int32_data);
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.INT32:
                             this._data = new Int32Array(tensor.int32_data);
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.UINT32:
                         case onnx.DataType.UINT64:
                             this._data = tensor.uint64_data;
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.INT64:
                             this._data = tensor.int64_data;
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.STRING:
                             this._data = tensor.string_data;
-                            this._layout = '|';
+                            this._encoding = '|';
                             break;
                         case onnx.DataType.COMPLEX64:
                         case onnx.DataType.COMPLEX128:
@@ -695,7 +694,7 @@ onnx.Tensor = class {
                                     view.setUint16(i << 1, array[i], true);
                                 }
                                 this._data = buffer;
-                                this._layout = '<';
+                                this._encoding = '<';
                             }
                             break;
                         case onnx.DataType.FLOAT8E4M3FN:
@@ -704,7 +703,7 @@ onnx.Tensor = class {
                         case onnx.DataType.FLOAT8E5M2FNUZ:
                             if (tensor.int32_data && tensor.int32_data.length > 0) {
                                 this._data = new Uint8Array(Array.from(tensor.int32_data));
-                                this._layout = '<';
+                                this._encoding = '<';
                             }
                             break;
                         default:
@@ -715,7 +714,7 @@ onnx.Tensor = class {
                     }
                     if (!this._data && tensor.raw_data && tensor.raw_data.length > 0) {
                         this._data = tensor.raw_data;
-                        this._layout = '<';
+                        this._encoding = '<';
                     }
                     break;
                 }
@@ -730,7 +729,7 @@ onnx.Tensor = class {
                             const length = parseInt(external_data.length, 10);
                             if (Number.isInteger(offset) && Number.isInteger(length)) {
                                 this._data = context.location(external_data.location, offset, length);
-                                this._layout = '<';
+                                this._encoding = '<';
                             }
                         }
                     }
@@ -751,8 +750,8 @@ onnx.Tensor = class {
         return this._category;
     }
 
-    get layout() {
-        return this._layout;
+    get encoding() {
+        return this._encoding;
     }
 
     get type() {
@@ -764,7 +763,7 @@ onnx.Tensor = class {
     }
 
     get values() {
-        switch (this._layout) {
+        switch (this.type.layout) {
             case 'sparse': {
                 return this._values;
             }
@@ -783,9 +782,10 @@ onnx.Tensor = class {
 
 onnx.TensorType = class {
 
-    constructor(dataType, shape, denotation) {
+    constructor(dataType, shape, layout, denotation) {
         this._dataType = dataType;
         this._shape = shape;
+        this._layout = layout || null;
         this._denotation = denotation || null;
     }
 
@@ -797,6 +797,10 @@ onnx.TensorType = class {
         return this._shape;
     }
 
+    get layout() {
+        return this._layout;
+    }
+
     get denotation() {
         return this._denotation;
     }
@@ -1312,11 +1316,11 @@ onnx.GraphContext = class {
         if (type.tensor_type) {
             const tensor_type = type.tensor_type;
             const shape = tensor_type.shape && tensor_type.shape.dim ? tensor_type.shape.dim.map((dim) => dim.dim_param ? dim.dim_param : dim.dim_value ? dim.dim_value : null) : [];
-            return this.createTensorType(tensor_type.elem_type, shape, denotation);
+            return this.createTensorType(tensor_type.elem_type, shape, null, denotation);
         } else if (type.sparse_tensor_type) {
-            const tensor_type = type.sparse_tensor_type;
-            const shape = tensor_type.shape && tensor_type.shape.dim ? tensor_type.shape.dim.map((dim) => dim.dim_param ? dim.dim_param : dim.dim_value ? dim.dim_value : null) : [];
-            return this.createTensorType(tensor_type.elem_type, shape, denotation);
+            type = type.sparse_tensor_type;
+            const shape = type.shape && type.shape.dim ? type.shape.dim.map((dim) => dim.dim_param ? dim.dim_param : dim.dim_value ? dim.dim_value : null) : [];
+            return this.createTensorType(type.elem_type, shape, 'sparse', denotation);
         } else if (type.map_type) {
             return this.createMapType(type.map_type.key_type, this.createType(type.map_type.value_type), denotation);
         } else if (type.sequence_type) {
@@ -1331,9 +1335,9 @@ onnx.GraphContext = class {
         throw new onnx.Error("Unsupported tensor type '" + JSON.stringify(type) + "'.");
     }
 
-    createTensorType(dataType, shape, denotation) {
+    createTensorType(dataType, shape, layout, denotation) {
         dataType = this.createDataType(dataType);
-        return new onnx.TensorType(dataType, new onnx.TensorShape(shape), denotation);
+        return new onnx.TensorType(dataType, new onnx.TensorShape(shape), layout, denotation);
     }
 
     createMapType(keyType, valueType, denotation) {
diff --git a/source/python.js b/source/python.js
index ac5f05046e0..b3acc718b01 100644
--- a/source/python.js
+++ b/source/python.js
@@ -2178,9 +2178,12 @@ python.Execution = class {
         });
         this.registerFunction('megengine.functional.nn.conv2d', function() {});
         this.registerFunction('megengine.functional.nn.relu', function() {});
+        this.registerFunction('megengine.functional.nn.sigmoid', function() {});
         this.registerFunction('megengine.module.qat.module.QATModule._apply_fakequant_with_observer', function() {});
         this.registerFunction('megengine.functional.tensor.concat', function() {});
         this.registerFunction('megengine.functional.tensor.flatten', function() {});
+        this.registerFunction('megengine.functional.tensor.split', function() {});
+        this.registerFunction('megengine.functional.tensor.reshape', function() {});
         this.registerType('megengine.core._imperative_rt.common.CompNode', class {});
         this.registerType('megengine.core._imperative_rt.ops.FakeQuant', class {});
         this.registerType('megengine.core._imperative_rt.ops.GetVarShape', class {});
diff --git a/source/pytorch-metadata.json b/source/pytorch-metadata.json
index bf8bf59bcee..dee7cc1bb61 100755
--- a/source/pytorch-metadata.json
+++ b/source/pytorch-metadata.json
@@ -829,7 +829,8 @@
       { "name": "dtype", "type": "ScalarType", "optional": true, "default": null },
       { "name": "layout", "type": "Layout", "optional": true, "default": null },
       { "name": "device", "type": "Device", "optional": true, "default": null },
-      { "name": "pin_memory", "type": "boolean", "optional": true, "default": null }
+      { "name": "pin_memory", "type": "boolean", "optional": true, "default": null },
+      { "name": "is_coalesced", "type": "boolean", "optional": true, "default": null }
     ],
     "outputs": [
       { "name": "outputs", "type": "Tensor" }
diff --git a/source/pytorch.js b/source/pytorch.js
index 4463263986f..68981f906c1 100644
--- a/source/pytorch.js
+++ b/source/pytorch.js
@@ -663,16 +663,16 @@ pytorch.Tensor = class {
         this._name = name || '';
         const storage = tensor.storage();
         const size = tensor.size();
-        this._type = new pytorch.TensorType(storage.dtype.__reduce__(), new pytorch.TensorShape(size));
         const layout = tensor.layout ? tensor.layout.__str__() : null;
         this._stride = tensor.stride();
         if (layout && layout.startsWith('torch.sparse_')) {
-            this._layout = layout.split('.').pop().replace('_', '.');
+            this._type = new pytorch.TensorType(storage.dtype.__reduce__(), new pytorch.TensorShape(size), layout.split('.').pop().replace('_', '.'));
             this._indices = new pytorch.Tensor('', tensor.indices);
             this._values = new pytorch.Tensor('', tensor.values);
         } else if (!layout || layout === 'torch.strided') {
+            this._type = new pytorch.TensorType(storage.dtype.__reduce__(), new pytorch.TensorShape(size));
             this._data = storage.data;
-            this._layout = '<';
+            this._encoding = '<';
             this._indices = null;
         } else {
             throw new pytorch.Error("Unsupported tensor layout '" + layout + "'.");
@@ -687,8 +687,8 @@ pytorch.Tensor = class {
         return this._type;
     }
 
-    get layout() {
-        return this._layout;
+    get encoding() {
+        return this._encoding;
     }
 
     get stride() {
@@ -700,15 +700,16 @@ pytorch.Tensor = class {
     }
 
     get values() {
-        if (this._layout && this._layout.startsWith('sparse.')) {
+        const type = this._type.layout;
+        if (type && type.startsWith('sparse.')) {
             return this._values;
         }
         return this._data instanceof Uint8Array ? this._data : this._data.peek();
     }
 
     decode() {
-        if (this._layout !== '<') {
-            throw new pytorch.Error("Tensor layout '" + this._layout + "' not implemented.");
+        if (this._encoding !== '<') {
+            throw new pytorch.Error("Tensor encoding '" + this._encoding + "' not implemented.");
         }
         const type = this._type;
         const data = this.values;
@@ -740,9 +741,10 @@ pytorch.Tensor = class {
 
 pytorch.TensorType = class {
 
-    constructor(dataType, shape) {
+    constructor(dataType, shape, layout) {
         this._dataType = dataType;
         this._shape = shape;
+        this._layout = layout;
     }
 
     get dataType() {
@@ -753,6 +755,10 @@ pytorch.TensorType = class {
         return this._shape;
     }
 
+    get layout() {
+        return this._layout;
+    }
+
     toString() {
         return this._dataType + this._shape.toString();
     }
@@ -2676,6 +2682,20 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     tensor.resize_(Array.isArray(tensor.shape) && tensor.shape.length > size ? tensor.shape.slice(-size) : Array(size).fill(NaN));
                 }
             }
+            // if torch.gt(torch.dim(x), 1):
+            //   xxxx
+            //   ops.prim.RaiseException(...)
+            if (statement.type === 'if' &&
+                pytorch.Utility.isCall(statement.condition, 'torch.gt', 2) &&
+                pytorch.Utility.isCall(statement.condition.args[0], 'torch.dim', 1) &&
+                statement.then.statements.length > 0 &&
+                pytorch.Utility.isCall(statement.then.statements.slice(-1).pop(), 'ops.prim.RaiseException')) {
+                const tensor = this.expression(statement.condition.args[0].args[0], context);
+                const size = this.expression(statement.condition.args[1], context);
+                if (pytorch.Utility.isTensor(tensor) && Number.isInteger(size) && size < 10) {
+                    tensor.resize_(Array.isArray(tensor.shape) && tensor.shape.length > size ? tensor.shape.slice(-size) : Array(size).fill(NaN));
+                }
+            }
             // if bool(...):
             //   ops.prim.RaiseException(torch.format(_1, dtype))
             // else:
@@ -3359,7 +3379,7 @@ pytorch.Utility = class {
 
     static isCall(expression, name, size) {
         if (expression.type === 'call' &&
-            expression.args.length === size &&
+            (size === undefined || size === expression.args.length) &&
             pytorch.Utility.target(expression.target) === name) {
             return true;
         }
@@ -4210,7 +4230,7 @@ pytorch.nnapi.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         return '<';
     }
 
diff --git a/source/rknn.js b/source/rknn.js
index 1e86623ea26..ee32998dad1 100644
--- a/source/rknn.js
+++ b/source/rknn.js
@@ -548,14 +548,14 @@ rknn.Container = class extends Map {
                         case 0x0002:
                         case 0x1002:
                         case 0x1003:
+                        case 0x0003:
                         case 0x0004:
+                        case 0x0005:
+                        case 0x0006:
                             if (data_size > 0) {
                                 stream.skip(40);
                             }
                             break;
-                        case 0x0003:
-                        case 0x0005:
-                        case 0x0006:
                         default:
                             throw new rknn.Error("Unsupported RKNN container version '" + version + "'.");
                     }
diff --git a/source/safetensors.js b/source/safetensors.js
index d776b1e26d2..ee9fed09d88 100644
--- a/source/safetensors.js
+++ b/source/safetensors.js
@@ -138,7 +138,7 @@ safetensors.Tensor = class {
     constructor(obj, position, stream) {
         const shape = new safetensors.TensorShape(obj.shape);
         this.type = new safetensors.TensorType(obj.dtype, shape);
-        this.layout = '<';
+        this.encoding = '<';
         const size = obj.data_offsets[1] - obj.data_offsets[0];
         position += obj.data_offsets[0];
         stream.seek(position);
diff --git a/source/sklearn.js b/source/sklearn.js
index 3f00d7308b0..aa6be3bb20d 100644
--- a/source/sklearn.js
+++ b/source/sklearn.js
@@ -259,7 +259,7 @@ sklearn.Tensor = class {
 
     constructor(array) {
         this.type = new sklearn.TensorType(array.dtype.__name__, new sklearn.TensorShape(array.shape));
-        this.layout = this.type.dataType == 'string' || this.type.dataType == 'object' ? '|' : array.dtype.byteorder;
+        this.encoding = this.type.dataType == 'string' || this.type.dataType == 'object' ? '|' : array.dtype.byteorder;
         this.values = this.type.dataType == 'string' || this.type.dataType == 'object' ? array.tolist() : array.tobytes();
     }
 };
diff --git a/source/tf-metadata.json b/source/tf-metadata.json
index 3ad11497ab9..e51b9c4c96f 100644
--- a/source/tf-metadata.json
+++ b/source/tf-metadata.json
@@ -8086,6 +8086,7 @@
   {
     "name": "CollectiveAllToAllV2",
     "summary": "Mutually exchanges multiple tensors of identical type and shape.",
+    "description": "`is_stateless` means each op does not need control dependencies to other\ncollective ops. In this case, keys that are unique at runtime\n(e.g. `instance_key`) should be used to distinguish collective groups.",
     "attributes": [
       {
         "name": "T",
@@ -8102,6 +8103,11 @@
         "type": "float32",
         "default": 0.0
       },
+      {
+        "name": "is_stateless",
+        "type": "boolean",
+        "default": false
+      },
       {
         "name": "Nordering_token",
         "type": "int64",
@@ -8446,6 +8452,7 @@
   {
     "name": "CollectiveGatherV2",
     "summary": "Mutually accumulates multiple tensors of identical type and shape.",
+    "description": "`is_stateless` means each op does not need control dependencies to other\ncollective ops. In this case, keys that are unique at runtime\n(e.g. `instance_key`) should be used to distinguish collective groups.",
     "attributes": [
       {
         "name": "T",
@@ -8462,6 +8469,11 @@
         "type": "float32",
         "default": 0.0
       },
+      {
+        "name": "is_stateless",
+        "type": "boolean",
+        "default": false
+      },
       {
         "name": "Nordering_token",
         "type": "int64",
@@ -8633,6 +8645,7 @@
   {
     "name": "CollectiveReduceScatterV2",
     "summary": "Mutually reduces multiple tensors of identical type and shape and scatters the result.",
+    "description": "`is_stateless` means each op does not need control dependencies to other\ncollective ops. In this case, keys that are unique at runtime\n(e.g. `instance_key`) should be used to distinguish collective groups.",
     "attributes": [
       {
         "name": "T",
@@ -8659,6 +8672,11 @@
         "type": "float32",
         "default": 0.0
       },
+      {
+        "name": "is_stateless",
+        "type": "boolean",
+        "default": false
+      },
       {
         "name": "Nordering_token",
         "type": "int64",
@@ -8704,6 +8722,7 @@
   {
     "name": "CollectiveReduceV2",
     "summary": "Mutually reduces multiple tensors of identical type and shape.",
+    "description": "`is_stateless` means each op does not need control dependencies to other\ncollective ops. In this case, keys that are unique at runtime\n(e.g. `instance_key`) should be used to distinguish collective groups.",
     "attributes": [
       {
         "name": "T",
@@ -8730,6 +8749,11 @@
         "type": "float32",
         "default": 0.0
       },
+      {
+        "name": "is_stateless",
+        "type": "boolean",
+        "default": false
+      },
       {
         "name": "Nordering_token",
         "type": "int64",
diff --git a/source/tf-proto.js b/source/tf-proto.js
index 37dfcc85f27..d0c3e70f071 100644
--- a/source/tf-proto.js
+++ b/source/tf-proto.js
@@ -6372,6 +6372,9 @@ $root.tensorflow.GPUOptions.Experimental = class Experimental {
                 case 1:
                     message.virtual_devices.push($root.tensorflow.GPUOptions.Experimental.VirtualDevices.decode(reader, reader.uint32()));
                     break;
+                case 15:
+                    message.num_virtual_devices_per_gpu = reader.int32();
+                    break;
                 case 2:
                     message.use_unified_memory = reader.bool();
                     break;
@@ -6425,6 +6428,9 @@ $root.tensorflow.GPUOptions.Experimental = class Experimental {
                 case "virtual_devices":
                     message.virtual_devices.push($root.tensorflow.GPUOptions.Experimental.VirtualDevices.decodeText(reader));
                     break;
+                case "num_virtual_devices_per_gpu":
+                    message.num_virtual_devices_per_gpu = reader.int32();
+                    break;
                 case "use_unified_memory":
                     message.use_unified_memory = reader.bool();
                     break;
@@ -6470,6 +6476,7 @@ $root.tensorflow.GPUOptions.Experimental = class Experimental {
     }
 };
 
+$root.tensorflow.GPUOptions.Experimental.prototype.num_virtual_devices_per_gpu = 0;
 $root.tensorflow.GPUOptions.Experimental.prototype.use_unified_memory = false;
 $root.tensorflow.GPUOptions.Experimental.prototype.num_dev_to_dev_copy_streams = 0;
 $root.tensorflow.GPUOptions.Experimental.prototype.collective_ring_order = "";
@@ -7058,6 +7065,9 @@ $root.tensorflow.ConfigProto.Experimental = class Experimental {
                 case 24:
                     message.disable_optimize_for_static_graph = reader.bool();
                     break;
+                case 26:
+                    message.disable_eager_executor_streaming_enqueue = reader.bool();
+                    break;
                 default:
                     reader.skipType(tag & 7);
                     break;
@@ -7135,6 +7145,9 @@ $root.tensorflow.ConfigProto.Experimental = class Experimental {
                 case "disable_optimize_for_static_graph":
                     message.disable_optimize_for_static_graph = reader.bool();
                     break;
+                case "disable_eager_executor_streaming_enqueue":
+                    message.disable_eager_executor_streaming_enqueue = reader.bool();
+                    break;
                 default:
                     reader.field(tag, message);
                     break;
@@ -7165,6 +7178,7 @@ $root.tensorflow.ConfigProto.Experimental.prototype.disable_functional_ops_lower
 $root.tensorflow.ConfigProto.Experimental.prototype.xla_prefer_single_graph_cluster = false;
 $root.tensorflow.ConfigProto.Experimental.prototype.coordination_config = null;
 $root.tensorflow.ConfigProto.Experimental.prototype.disable_optimize_for_static_graph = false;
+$root.tensorflow.ConfigProto.Experimental.prototype.disable_eager_executor_streaming_enqueue = false;
 
 $root.tensorflow.ConfigProto.Experimental.MlirBridgeRollout = {
     "MLIR_BRIDGE_ROLLOUT_UNSPECIFIED": 0,
@@ -9636,6 +9650,9 @@ $root.tensorflow.CoordinationServiceConfig = class CoordinationServiceConfig {
                 case 11:
                     message.allow_new_incarnation_to_reconnect = reader.bool();
                     break;
+                case 12:
+                    message.force_disable = reader.bool();
+                    break;
                 default:
                     reader.skipType(tag & 7);
                     break;
@@ -9680,6 +9697,9 @@ $root.tensorflow.CoordinationServiceConfig = class CoordinationServiceConfig {
                 case "allow_new_incarnation_to_reconnect":
                     message.allow_new_incarnation_to_reconnect = reader.bool();
                     break;
+                case "force_disable":
+                    message.force_disable = reader.bool();
+                    break;
                 default:
                     reader.field(tag, message);
                     break;
@@ -9697,6 +9717,7 @@ $root.tensorflow.CoordinationServiceConfig.prototype.heartbeat_timeout_in_ms = p
 $root.tensorflow.CoordinationServiceConfig.prototype.shutdown_barrier_timeout_in_ms = protobuf.Int64.create(0);
 $root.tensorflow.CoordinationServiceConfig.prototype.agent_destruction_without_shutdown = false;
 $root.tensorflow.CoordinationServiceConfig.prototype.allow_new_incarnation_to_reconnect = false;
+$root.tensorflow.CoordinationServiceConfig.prototype.force_disable = false;
 
 $root.tensorflow.MemmappedFileSystemDirectoryElement = class MemmappedFileSystemDirectoryElement {
 
diff --git a/source/tf.js b/source/tf.js
index 850a39163b7..de14344eba5 100644
--- a/source/tf.js
+++ b/source/tf.js
@@ -1225,7 +1225,7 @@ tf.Tensor = class {
             this._tensor = tensor;
             if (Object.prototype.hasOwnProperty.call(tensor, 'tensor_content')) {
                 this._values = tensor.tensor_content;
-                this._layout = '<';
+                this._encoding = '<';
             } else {
                 const DataType = tf.proto.tensorflow.DataType;
                 switch (tensor.dtype) {
@@ -1239,7 +1239,7 @@ tf.Tensor = class {
                         for (let i = 0; i < values.length; i++) {
                             view.setUint32(i << 2, values[i] << 16, true);
                         }
-                        this._layout = '<';
+                        this._encoding = '<';
                         break;
                     }
                     case DataType.DT_HALF: {
@@ -1249,17 +1249,17 @@ tf.Tensor = class {
                         for (let i = 0; i < values.length; i++) {
                             view.setUint16(i << 1, values[i], true);
                         }
-                        this._layout = '<';
+                        this._encoding = '<';
                         break;
                     }
                     case DataType.DT_FLOAT: {
                         this._values = tensor.float_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_DOUBLE: {
                         this._values = tensor.double_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_UINT8:
@@ -1268,36 +1268,36 @@ tf.Tensor = class {
                     case DataType.DT_INT16:
                     case DataType.DT_INT32: {
                         this._values = tensor.int_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_UINT32: {
                         this._values = tensor.uint32_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_INT64: {
                         this._values = tensor.int64_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_UINT64: {
                         this._values = tensor.uint64_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_BOOL: {
                         this._values = tensor.bool_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_STRING: {
                         this._values = tensor.string_val || null;
-                        this._layout = '|';
+                        this._encoding = '|';
                         break;
                     }
                     case DataType.DT_COMPLEX64: {
-                        this._layout = '|';
+                        this._encoding = '|';
                         const values = tensor.scomplex_val || null;
                         this._values = new Array(values.length >> 1);
                         for (let i = 0; i < values.length; i += 2) {
@@ -1306,7 +1306,7 @@ tf.Tensor = class {
                         break;
                     }
                     case DataType.DT_COMPLEX128: {
-                        this._layout = '|';
+                        this._encoding = '|';
                         const values = tensor.dcomplex_val || null;
                         this._values = new Array(values.length >> 1);
                         for (let i = 0; i < values.length; i += 2) {
@@ -1337,13 +1337,13 @@ tf.Tensor = class {
         return this._category;
     }
 
-    get layout() {
-        return this._layout;
+    get encoding() {
+        return this._encoding;
     }
 
     get values() {
         let values = this._values;
-        if (this._layout === '|' && Array.isArray(values)) {
+        if (this._encoding === '|' && Array.isArray(values)) {
             if (this._type.dataType === 'string') {
                 values = values.map((value) => tf.Utility.decodeText(value));
             }
diff --git a/source/tflite-metadata.json b/source/tflite-metadata.json
index baf82bdffd9..8a6cee7935c 100644
--- a/source/tflite-metadata.json
+++ b/source/tflite-metadata.json
@@ -833,6 +833,13 @@
       { "name": "broadcast_dimensions", "type": "int64[]", "default": 0 }
     ]
   },
+  {
+    "name": "StablehloCompare",
+    "attributes": [
+      { "name": "comparison_direction", "type": "StablehloComparisonDirection", "default": "STABLEHLO_COMPARISON_DIRECTION_EQ" },
+      { "name": "compare_type", "type": "StablehloComparisonType", "default": "STABLEHLO_COMPARISON_TYPE_NOTYPE" }
+    ]
+  },
   {
     "name": "StablehloConcatenate",
     "attributes": [
@@ -861,6 +868,88 @@
       { "name": "precision_config", "type": "StablehloPrecisionConfig[]", "default": "DEFAULT" }
     ]
   },
+  {
+    "name": "StablehloCustomCall",
+    "attributes": [
+      { "name": "call_target_name", "type": "string", "default": null },
+      { "name": "has_side_effect", "type": "boolean", "default": false },
+      { "name": "backend_config", "type": "string", "default": null },
+      { "name": "api_version", "type": "int32", "default": 0 },
+      { "name": "called_computations", "type": "int32[]", "default": 0 },
+      { "name": "custom_attributes", "type": "uint8[]", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloDotGeneral",
+    "attributes": [
+      { "name": "lhs_batching_dimensions", "type": "int64[]", "default": 0 },
+      { "name": "rhs_batching_dimensions", "type": "int64[]", "default": 0 },
+      { "name": "lhs_contracting_dimensions", "type": "int64[]", "default": 0 },
+      { "name": "rhs_contracting_dimensions", "type": "int64[]", "default": 0 },
+      { "name": "precision_config", "type": "StablehloPrecisionConfig[]", "default": "DEFAULT" }
+    ]
+  },
+  {
+    "name": "StablehloDynamicSlice",
+    "attributes": [
+      { "name": "slice_sizes", "type": "int64[]", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloGather",
+    "attributes": [
+      { "name": "offset_dims", "type": "int64[]", "default": 0 },
+      { "name": "collapsed_slice_dims", "type": "int64[]", "default": 0 },
+      { "name": "start_index_map", "type": "int64[]", "default": 0 },
+      { "name": "index_vector_dim", "type": "int64", "default": 0 },
+      { "name": "slice_sizes", "type": "int64[]", "default": 0 },
+      { "name": "indices_are_sorted", "type": "boolean", "default": false }
+    ]
+  },
+  {
+    "name": "StablehloIota",
+    "attributes": [
+      { "name": "iota_dimension", "type": "int64", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloPad",
+    "attributes": [
+      { "name": "edge_padding_low", "type": "int64[]", "default": 0 },
+      { "name": "edge_padding_high", "type": "int64[]", "default": 0 },
+      { "name": "interior_padding", "type": "int64[]", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloReduce",
+    "attributes": [
+      { "name": "dimensions", "type": "int64[]", "default": 0 },
+      { "name": "body_subgraph_index", "type": "int32", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloReduceWindow",
+    "attributes": [
+      { "name": "window_dimensions", "type": "int64[]", "default": 0 },
+      { "name": "window_strides", "type": "int64[]", "default": 0 },
+      { "name": "base_dilations", "type": "int64[]", "default": 0 },
+      { "name": "window_dilations", "type": "int64[]", "default": 0 },
+      { "name": "padding", "type": "int64[]", "default": 0 },
+      { "name": "body_subgraph_index", "type": "int32", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloScatter",
+    "attributes": [
+      { "name": "indices_are_sorted", "type": "boolean", "default": false },
+      { "name": "update_window_dims", "type": "int64[]", "default": 0 },
+      { "name": "inserted_window_dims", "type": "int64[]", "default": 0 },
+      { "name": "scatter_dims_to_operand_dims", "type": "int64[]", "default": 0 },
+      { "name": "index_vector_dim", "type": "int64", "default": 0 },
+      { "name": "unique_indices", "type": "boolean", "default": false },
+      { "name": "update_computation_subgraph_index", "type": "int32", "default": 0 }
+    ]
+  },
   {
     "name": "StablehloSlice",
     "attributes": [
@@ -869,6 +958,27 @@
       { "name": "strides", "type": "int64[]", "default": 0 }
     ]
   },
+  {
+    "name": "StablehloSort",
+    "attributes": [
+      { "name": "dimension", "type": "int64", "default": 0 },
+      { "name": "is_stable", "type": "boolean", "default": false },
+      { "name": "comparator_subgraph_index", "type": "int32", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloTranspose",
+    "attributes": [
+      { "name": "permutation", "type": "int64[]", "default": 0 }
+    ]
+  },
+  {
+    "name": "StablehloWhile",
+    "attributes": [
+      { "name": "cond_subgraph_index", "type": "int32", "default": 0 },
+      { "name": "body_subgraph_index", "type": "int32", "default": 0 }
+    ]
+  },
   {
     "name": "StridedSlice",
     "category": "Tensor",
diff --git a/source/tflite-schema.js b/source/tflite-schema.js
index fecf559e979..72bb28681de 100644
--- a/source/tflite-schema.js
+++ b/source/tflite-schema.js
@@ -416,7 +416,37 @@ $root.tflite.BuiltinOperator = {
     STABLEHLO_CONCATENATE: 169,
     STABLEHLO_BROADCAST_IN_DIM: 170,
     STABLEHLO_CONVOLUTION: 171,
-    STABLEHLO_SLICE: 172
+    STABLEHLO_SLICE: 172,
+    STABLEHLO_CUSTOM_CALL: 173,
+    STABLEHLO_REDUCE: 174,
+    STABLEHLO_ABS: 175,
+    STABLEHLO_AND: 176,
+    STABLEHLO_COSINE: 177,
+    STABLEHLO_EXPONENTIAL: 178,
+    STABLEHLO_FLOOR: 179,
+    STABLEHLO_LOG: 180,
+    STABLEHLO_MINIMUM: 181,
+    STABLEHLO_NEGATE: 182,
+    STABLEHLO_OR: 183,
+    STABLEHLO_POWER: 184,
+    STABLEHLO_REMAINDER: 185,
+    STABLEHLO_RSQRT: 186,
+    STABLEHLO_SELECT: 187,
+    STABLEHLO_SUBTRACT: 188,
+    STABLEHLO_TANH: 189,
+    STABLEHLO_SCATTER: 190,
+    STABLEHLO_COMPARE: 191,
+    STABLEHLO_CONVERT: 192,
+    STABLEHLO_DYNAMIC_SLICE: 193,
+    STABLEHLO_DYNAMIC_UPDATE_SLICE: 194,
+    STABLEHLO_PAD: 195,
+    STABLEHLO_IOTA: 196,
+    STABLEHLO_DOT_GENERAL: 197,
+    STABLEHLO_REDUCE_WINDOW: 198,
+    STABLEHLO_SORT: 199,
+    STABLEHLO_WHILE: 200,
+    STABLEHLO_GATHER: 201,
+    STABLEHLO_TRANSPOSE: 202
 };
 
 $root.tflite.BuiltinOptions = class {
@@ -694,6 +724,19 @@ $root.tflite.BuiltinOptions2 = class {
             case 2: return $root.tflite.StablehloBroadcastInDimOptions.decode(reader, position);
             case 3: return $root.tflite.StablehloSliceOptions.decode(reader, position);
             case 4: return $root.tflite.StablehloConvolutionOptions.decode(reader, position);
+            case 5: return $root.tflite.StablehloCustomCallOptions.decode(reader, position);
+            case 6: return $root.tflite.StablehloReduceOptions.decode(reader, position);
+            case 7: return $root.tflite.StablehloScatterOptions.decode(reader, position);
+            case 8: return $root.tflite.StablehloCompareOptions.decode(reader, position);
+            case 9: return $root.tflite.StablehloDynamicSliceOptions.decode(reader, position);
+            case 10: return $root.tflite.StablehloPadOptions.decode(reader, position);
+            case 11: return $root.tflite.StablehloIotaOptions.decode(reader, position);
+            case 12: return $root.tflite.StablehloDotGeneralOptions.decode(reader, position);
+            case 13: return $root.tflite.StablehloReduceWindowOptions.decode(reader, position);
+            case 14: return $root.tflite.StablehloSortOptions.decode(reader, position);
+            case 15: return $root.tflite.StablehloWhileOptions.decode(reader, position);
+            case 16: return $root.tflite.StablehloGatherOptions.decode(reader, position);
+            case 17: return $root.tflite.StablehloTransposeOptions.decode(reader, position);
             default: return undefined;
         }
     }
@@ -704,17 +747,154 @@ $root.tflite.BuiltinOptions2 = class {
             case 'StablehloBroadcastInDimOptions': return $root.tflite.StablehloBroadcastInDimOptions.decodeText(reader, json);
             case 'StablehloSliceOptions': return $root.tflite.StablehloSliceOptions.decodeText(reader, json);
             case 'StablehloConvolutionOptions': return $root.tflite.StablehloConvolutionOptions.decodeText(reader, json);
+            case 'StablehloCustomCallOptions': return $root.tflite.StablehloCustomCallOptions.decodeText(reader, json);
+            case 'StablehloReduceOptions': return $root.tflite.StablehloReduceOptions.decodeText(reader, json);
+            case 'StablehloScatterOptions': return $root.tflite.StablehloScatterOptions.decodeText(reader, json);
+            case 'StablehloCompareOptions': return $root.tflite.StablehloCompareOptions.decodeText(reader, json);
+            case 'StablehloDynamicSliceOptions': return $root.tflite.StablehloDynamicSliceOptions.decodeText(reader, json);
+            case 'StablehloPadOptions': return $root.tflite.StablehloPadOptions.decodeText(reader, json);
+            case 'StablehloIotaOptions': return $root.tflite.StablehloIotaOptions.decodeText(reader, json);
+            case 'StablehloDotGeneralOptions': return $root.tflite.StablehloDotGeneralOptions.decodeText(reader, json);
+            case 'StablehloReduceWindowOptions': return $root.tflite.StablehloReduceWindowOptions.decodeText(reader, json);
+            case 'StablehloSortOptions': return $root.tflite.StablehloSortOptions.decodeText(reader, json);
+            case 'StablehloWhileOptions': return $root.tflite.StablehloWhileOptions.decodeText(reader, json);
+            case 'StablehloGatherOptions': return $root.tflite.StablehloGatherOptions.decodeText(reader, json);
+            case 'StablehloTransposeOptions': return $root.tflite.StablehloTransposeOptions.decodeText(reader, json);
             default: return undefined;
         }
     }
 };
 
+$root.tflite.StablehloGatherOptions = class StablehloGatherOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloGatherOptions();
+        $.offset_dims = reader.int64s_(position, 4);
+        $.collapsed_slice_dims = reader.int64s_(position, 6);
+        $.start_index_map = reader.int64s_(position, 8);
+        $.index_vector_dim = reader.int64_(position, 10, 0);
+        $.slice_sizes = reader.int64s_(position, 12);
+        $.indices_are_sorted = reader.bool_(position, 14, false);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloGatherOptions();
+        $.offset_dims = reader.array(json.offset_dims);
+        $.collapsed_slice_dims = reader.array(json.collapsed_slice_dims);
+        $.start_index_map = reader.array(json.start_index_map);
+        $.index_vector_dim = reader.value(json.index_vector_dim, 0);
+        $.slice_sizes = reader.array(json.slice_sizes);
+        $.indices_are_sorted = reader.value(json.indices_are_sorted, false);
+        return $;
+    }
+};
+
+$root.tflite.StablehloTransposeOptions = class StablehloTransposeOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloTransposeOptions();
+        $.permutation = reader.int64s_(position, 4);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloTransposeOptions();
+        $.permutation = reader.array(json.permutation);
+        return $;
+    }
+};
+
 $root.tflite.StablehloPrecisionConfig = {
     DEFAULT: 0,
     HIGH: 1,
     HIGHEST: 2
 };
 
+$root.tflite.StablehloDotGeneralOptions = class StablehloDotGeneralOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloDotGeneralOptions();
+        $.lhs_batching_dimensions = reader.int64s_(position, 4);
+        $.rhs_batching_dimensions = reader.int64s_(position, 6);
+        $.lhs_contracting_dimensions = reader.int64s_(position, 8);
+        $.rhs_contracting_dimensions = reader.int64s_(position, 10);
+        $.precision_config = reader.typedArray(position, 12, Uint32Array);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloDotGeneralOptions();
+        $.lhs_batching_dimensions = reader.array(json.lhs_batching_dimensions);
+        $.rhs_batching_dimensions = reader.array(json.rhs_batching_dimensions);
+        $.lhs_contracting_dimensions = reader.array(json.lhs_contracting_dimensions);
+        $.rhs_contracting_dimensions = reader.array(json.rhs_contracting_dimensions);
+        $.precision_config = reader.objectArray(json.precision_config, $root.tflite.StablehloPrecisionConfig.decodeText);
+        return $;
+    }
+};
+
+$root.tflite.StablehloReduceWindowOptions = class StablehloReduceWindowOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloReduceWindowOptions();
+        $.window_dimensions = reader.int64s_(position, 4);
+        $.window_strides = reader.int64s_(position, 6);
+        $.base_dilations = reader.int64s_(position, 8);
+        $.window_dilations = reader.int64s_(position, 10);
+        $.padding = reader.int64s_(position, 12);
+        $.body_subgraph_index = reader.int32_(position, 14, 0);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloReduceWindowOptions();
+        $.window_dimensions = reader.array(json.window_dimensions);
+        $.window_strides = reader.array(json.window_strides);
+        $.base_dilations = reader.array(json.base_dilations);
+        $.window_dilations = reader.array(json.window_dilations);
+        $.padding = reader.array(json.padding);
+        $.body_subgraph_index = reader.value(json.body_subgraph_index, 0);
+        return $;
+    }
+};
+
+$root.tflite.StablehloWhileOptions = class StablehloWhileOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloWhileOptions();
+        $.cond_subgraph_index = reader.int32_(position, 4, 0);
+        $.body_subgraph_index = reader.int32_(position, 6, 0);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloWhileOptions();
+        $.cond_subgraph_index = reader.value(json.cond_subgraph_index, 0);
+        $.body_subgraph_index = reader.value(json.body_subgraph_index, 0);
+        return $;
+    }
+};
+
+$root.tflite.StablehloSortOptions = class StablehloSortOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloSortOptions();
+        $.dimension = reader.int64_(position, 4, 0);
+        $.is_stable = reader.bool_(position, 6, false);
+        $.comparator_subgraph_index = reader.int32_(position, 8, 0);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloSortOptions();
+        $.dimension = reader.value(json.dimension, 0);
+        $.is_stable = reader.value(json.is_stable, false);
+        $.comparator_subgraph_index = reader.value(json.comparator_subgraph_index, 0);
+        return $;
+    }
+};
+
 $root.tflite.StablehloConcatenateOptions = class StablehloConcatenateOptions {
 
     static decode(reader, position) {
@@ -745,6 +925,131 @@ $root.tflite.StablehloBroadcastInDimOptions = class StablehloBroadcastInDimOptio
     }
 };
 
+$root.tflite.StablehloComparisonDirection = {
+    STABLEHLO_COMPARISON_DIRECTION_EQ: 0,
+    STABLEHLO_COMPARISON_DIRECTION_NE: 1,
+    STABLEHLO_COMPARISON_DIRECTION_GE: 2,
+    STABLEHLO_COMPARISON_DIRECTION_GT: 3,
+    STABLEHLO_COMPARISON_DIRECTION_LE: 4,
+    STABLEHLO_COMPARISON_DIRECTION_LT: 5
+};
+
+$root.tflite.StablehloComparisonType = {
+    STABLEHLO_COMPARISON_TYPE_NOTYPE: 0,
+    STABLEHLO_COMPARISON_TYPE_FLOAT: 1,
+    STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER: 2,
+    STABLEHLO_COMPARISON_TYPE_SIGNED: 3,
+    STABLEHLO_COMPARISON_TYPE_UNSIGNED: 4
+};
+
+$root.tflite.StablehloCompareOptions = class StablehloCompareOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloCompareOptions();
+        $.comparison_direction = reader.uint32_(position, 4, 0);
+        $.compare_type = reader.uint32_(position, 6, 0);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloCompareOptions();
+        $.comparison_direction = $root.tflite.StablehloComparisonDirection[json.comparison_direction];
+        $.compare_type = $root.tflite.StablehloComparisonType[json.compare_type];
+        return $;
+    }
+};
+
+$root.tflite.StablehloDynamicSliceOptions = class StablehloDynamicSliceOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloDynamicSliceOptions();
+        $.slice_sizes = reader.int64s_(position, 4);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloDynamicSliceOptions();
+        $.slice_sizes = reader.array(json.slice_sizes);
+        return $;
+    }
+};
+
+$root.tflite.StablehloPadOptions = class StablehloPadOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloPadOptions();
+        $.edge_padding_low = reader.int64s_(position, 4);
+        $.edge_padding_high = reader.int64s_(position, 6);
+        $.interior_padding = reader.int64s_(position, 8);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloPadOptions();
+        $.edge_padding_low = reader.array(json.edge_padding_low);
+        $.edge_padding_high = reader.array(json.edge_padding_high);
+        $.interior_padding = reader.array(json.interior_padding);
+        return $;
+    }
+};
+
+$root.tflite.StablehloIotaOptions = class StablehloIotaOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloIotaOptions();
+        $.iota_dimension = reader.int64_(position, 4, 0);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloIotaOptions();
+        $.iota_dimension = reader.value(json.iota_dimension, 0);
+        return $;
+    }
+};
+
+$root.tflite.StablehloCustomCallOptions = class StablehloCustomCallOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloCustomCallOptions();
+        $.call_target_name = reader.string_(position, 4, null);
+        $.has_side_effect = reader.bool_(position, 6, false);
+        $.backend_config = reader.string_(position, 8, null);
+        $.api_version = reader.int32_(position, 10, 0);
+        $.called_computations = reader.typedArray(position, 12, Int32Array);
+        $.custom_attributes = reader.typedArray(position, 14, Uint8Array);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloCustomCallOptions();
+        $.call_target_name = reader.value(json.call_target_name, null);
+        $.has_side_effect = reader.value(json.has_side_effect, false);
+        $.backend_config = reader.value(json.backend_config, null);
+        $.api_version = reader.value(json.api_version, 0);
+        $.called_computations = reader.typedArray(json.called_computations, Int32Array);
+        $.custom_attributes = reader.typedArray(json.custom_attributes, Uint8Array);
+        return $;
+    }
+};
+
+$root.tflite.StablehloReduceOptions = class StablehloReduceOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloReduceOptions();
+        $.dimensions = reader.int64s_(position, 4);
+        $.body_subgraph_index = reader.int32_(position, 6, 0);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloReduceOptions();
+        $.dimensions = reader.array(json.dimensions);
+        $.body_subgraph_index = reader.value(json.body_subgraph_index, 0);
+        return $;
+    }
+};
+
 $root.tflite.StablehloSliceOptions = class StablehloSliceOptions {
 
     static decode(reader, position) {
@@ -811,6 +1116,33 @@ $root.tflite.StablehloConvolutionOptions = class StablehloConvolutionOptions {
     }
 };
 
+$root.tflite.StablehloScatterOptions = class StablehloScatterOptions {
+
+    static decode(reader, position) {
+        const $ = new $root.tflite.StablehloScatterOptions();
+        $.indices_are_sorted = reader.bool_(position, 4, false);
+        $.update_window_dims = reader.int64s_(position, 6);
+        $.inserted_window_dims = reader.int64s_(position, 8);
+        $.scatter_dims_to_operand_dims = reader.int64s_(position, 10);
+        $.index_vector_dim = reader.int64_(position, 12, 0);
+        $.unique_indices = reader.bool_(position, 14, false);
+        $.update_computation_subgraph_index = reader.int32_(position, 16, 0);
+        return $;
+    }
+
+    static decodeText(reader, json) {
+        const $ = new $root.tflite.StablehloScatterOptions();
+        $.indices_are_sorted = reader.value(json.indices_are_sorted, false);
+        $.update_window_dims = reader.array(json.update_window_dims);
+        $.inserted_window_dims = reader.array(json.inserted_window_dims);
+        $.scatter_dims_to_operand_dims = reader.array(json.scatter_dims_to_operand_dims);
+        $.index_vector_dim = reader.value(json.index_vector_dim, 0);
+        $.unique_indices = reader.value(json.unique_indices, false);
+        $.update_computation_subgraph_index = reader.value(json.update_computation_subgraph_index, 0);
+        return $;
+    }
+};
+
 $root.tflite.Padding = {
     SAME: 0,
     VALID: 1
diff --git a/source/tflite.js b/source/tflite.js
index 0f9df6da539..75120993d62 100644
--- a/source/tflite.js
+++ b/source/tflite.js
@@ -569,7 +569,7 @@ tflite.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         switch (this._type.dataType) {
             case 'string': return '|';
             default: return '<';
diff --git a/source/torch.js b/source/torch.js
index 97d39870ea6..a443af2abd0 100644
--- a/source/torch.js
+++ b/source/torch.js
@@ -490,7 +490,7 @@ torch.Tensor = class {
         return this._type;
     }
 
-    get layout() {
+    get encoding() {
         return '|';
     }
 
diff --git a/source/view.js b/source/view.js
index 44d12b31036..2b902f5ff2f 100644
--- a/source/view.js
+++ b/source/view.js
@@ -1225,7 +1225,7 @@ view.Menu = class {
             this.unregister(this._pop);
             this.unregister(this._push);
             this._element.style.opacity = 0;
-            this._element.style.left = '-16em';
+            this._element.style.left = '-17em';
             const button = this._element.ownerDocument.activeElement;
             if (this._buttons.indexOf(button) > 0) {
                 button.blur();
@@ -1593,7 +1593,6 @@ view.Graph = class extends grapher.Graph {
     createNode(node) {
         const value = new view.Node(this, node);
         value.name = (this._nodeKey++).toString();
-        this.setNode(value);
         this._table.set(node, value);
         return value;
     }
@@ -1601,7 +1600,6 @@ view.Graph = class extends grapher.Graph {
     createInput(input) {
         const value = new view.Input(this, input);
         value.name = (this._nodeKey++).toString();
-        this.setNode(value);
         this._table.set(input, value);
         return value;
     }
@@ -1609,7 +1607,6 @@ view.Graph = class extends grapher.Graph {
     createOutput(output) {
         const value = new view.Output(this, output);
         value.name = (this._nodeKey++).toString();
-        this.setNode(value);
         this._table.set(output, value);
         return value;
     }
@@ -1646,12 +1643,14 @@ view.Graph = class extends grapher.Graph {
         }
         for (const input of graph.inputs) {
             const viewInput = this.createInput(input);
+            this.setNode(viewInput);
             for (const value of input.value) {
                 this.createValue(value).from = viewInput;
             }
         }
         for (const node of graph.nodes) {
             const viewNode = this.createNode(node);
+            this.setNode(viewNode);
             const inputs = node.inputs;
             for (const input of inputs) {
                 for (const value of input.value) {
@@ -1719,6 +1718,7 @@ view.Graph = class extends grapher.Graph {
         }
         for (const output of graph.outputs) {
             const viewOutput = this.createOutput(output);
+            this.setNode(viewOutput);
             for (const value of output.value) {
                 this.createValue(value).to.push(viewOutput);
             }
@@ -1909,10 +1909,12 @@ view.Node = class extends grapher.Node {
         }
         if (Array.isArray(node.chain) && node.chain.length > 0) {
             for (const innerNode of node.chain) {
+                this.context.createNode(innerNode);
                 this._add(innerNode);
             }
         }
         if (node.inner) {
+            this.context.createNode(node.inner);
             this._add(node.inner);
         }
         if (node.nodes) {
@@ -2752,6 +2754,18 @@ view.ValueView = class extends view.Control {
                 if (location !== undefined) {
                     this._bold('location', location);
                 }
+                const layout = this._value.type ? this._value.type.layout : null;
+                if (layout) {
+                    const layouts = new Map([
+                        [ 'sparse', 'sparse' ],
+                        [ 'sparse.coo', 'sparse coo' ],
+                        [ 'sparse.csr', 'sparse csr' ],
+                        [ 'sparse.csc', 'sparse csc' ],
+                        [ 'sparse.bsr', 'sparse bsr' ],
+                        [ 'sparse.bsc', 'sparse bsc' ]
+                    ]);
+                    this._bold('layout', layouts.get(layout));
+                }
                 if (initializer) {
                     this._tensor(initializer);
                 }
@@ -2785,24 +2799,12 @@ view.ValueView = class extends view.Control {
         const contentLine = this.createElement('pre');
         try {
             const tensor = new view.Tensor(value);
-            const layout = tensor.layout;
-            if (layout) {
-                const layouts = new Map([
-                    [ 'sparse', 'Sparse' ],
-                    [ 'sparse.coo', 'Sparse COO' ],
-                    [ 'sparse.csr', 'Sparse CSR' ],
-                    [ 'sparse.csc', 'Sparse CSC' ],
-                    [ 'sparse.bsr', 'Sparse BSR' ],
-                    [ 'sparse.bsc', 'Sparse BSC' ]
-                ]);
-                if (layouts.has(layout)) {
-                    this._bold('layout', layouts.get(layout));
-                }
-            }
             if (Array.isArray(tensor.stride) && tensor.stride.length > 0) {
                 this._code('stride', tensor.stride.join(','));
             }
-            if (tensor.layout !== '<' && tensor.layout !== '>' && tensor.layout !== '|' && tensor.layout !== 'sparse' && tensor.layout !== 'sparse.coo') {
+            if (tensor.encoding !== '<' && tensor.encoding !== '>' && tensor.encoding !== '|') {
+                contentLine.innerHTML = "Tensor encoding '" + tensor.layout + "' is not implemented.";
+            } else if (tensor.layout && (tensor.layout !== 'sparse' && tensor.layout !== 'sparse.coo')) {
                 contentLine.innerHTML = "Tensor layout '" + tensor.layout + "' is not implemented.";
             } else if (tensor.empty) {
                 contentLine.innerHTML = 'Tensor data is empty.';
@@ -3310,40 +3312,40 @@ view.Tensor = class {
         this._tensor = tensor;
         this._type = tensor.type;
         this._stride = tensor.stride;
-        switch (tensor.layout) {
+        this._encoding = tensor.encoding;
+        this._layout = tensor.type.layout;
+        switch (this._encoding) {
             case undefined:
             case '':
             case '<': {
                 this._data = this._tensor.values;
-                this._layout = '<';
+                this._encoding = '<';
                 this._littleEndian = true;
                 break;
             }
             case '>': {
                 this._data = this._tensor.values;
-                this._layout = '>';
+                this._encoding = '>';
                 this._littleEndian = false;
                 break;
             }
             case '|': {
                 this._values = this._tensor.values;
-                this._layout = '|';
+                this._encoding = '|';
                 break;
             }
-            case 'sparse': {
-                this._indices = this._tensor.indices;
-                this._values = this._tensor.values;
-                this._layout = 'sparse';
-                break;
+            default: {
+                throw new view.Error("Unsupported tensor encoding '" + this._encoding + "'.");
             }
+        }
+        switch (this._layout) {
+            case 'sparse':
             case 'sparse.coo': {
                 this._indices = this._tensor.indices;
                 this._values = this._tensor.values;
-                this._layout = 'sparse.coo';
                 break;
             }
             default: {
-                this._layout = tensor.layout;
                 break;
             }
         }
@@ -3364,6 +3366,10 @@ view.Tensor = class {
         return this._type;
     }
 
+    get encoding() {
+        return this._encoding;
+    }
+
     get layout() {
         return this._layout;
     }
@@ -3374,19 +3380,20 @@ view.Tensor = class {
 
     get empty() {
         switch (this._layout) {
-            case '<':
-            case '>': {
-                return !(Array.isArray(this._data) || this._data instanceof Uint8Array || this._data instanceof Int8Array) || this._data.length === 0;
-            }
-            case '|': {
-                return !(Array.isArray(this._values) || ArrayBuffer.isView(this._values)) || this._values.length === 0;
-            }
             case 'sparse':
             case 'sparse.coo': {
                 return !this._values || this.indices || this._values.values.length === 0;
             }
             default: {
-                throw new Error("Unsupported tensor format '" + this._format + "'.");
+                switch (this._encoding) {
+                    case '<':
+                    case '>':
+                        return !(Array.isArray(this._data) || this._data instanceof Uint8Array || this._data instanceof Int8Array) || this._data.length === 0;
+                    case '|':
+                        return !(Array.isArray(this._values) || ArrayBuffer.isView(this._values)) || this._values.length === 0;
+                    default:
+                        throw new Error("Unsupported tensor encoding '" + this._encoding + "'.");
+                }
             }
         }
     }
@@ -3394,7 +3401,7 @@ view.Tensor = class {
     get value() {
         const context = this._context();
         context.limit = Number.MAX_SAFE_INTEGER;
-        switch (context.layout) {
+        switch (context.encoding) {
             case '<':
             case '>': {
                 return this._decodeData(context, 0);
@@ -3403,7 +3410,7 @@ view.Tensor = class {
                 return this._decodeValues(context, 0);
             }
             default: {
-                throw new Error("Unsupported tensor format '" + this._format + "'.");
+                throw new Error("Unsupported tensor encoding '" + context.encoding + "'.");
             }
         }
     }
@@ -3411,7 +3418,7 @@ view.Tensor = class {
     toString() {
         const context = this._context();
         context.limit = 10000;
-        switch (context.layout) {
+        switch (context.encoding) {
             case '<':
             case '>': {
                 const value = this._decodeData(context, 0);
@@ -3422,59 +3429,30 @@ view.Tensor = class {
                 return view.Tensor._stringify(value, '', '    ');
             }
             default: {
-                throw new Error("Unsupported tensor format '" + this._format + "'.");
+                throw new Error("Unsupported tensor encoding '" + context.encoding + "'.");
             }
         }
     }
 
     _context() {
-        if (this._layout !== '<' && this._layout !== '>' && this._layout !== '|' && this._layout !== 'sparse' && this._layout !== 'sparse.coo') {
+        if (this._encoding !== '<' && this._encoding !== '>' && this._encoding !== '|') {
+            throw new Error("Tensor encoding '" + this._encoding + "' is not supported.");
+        }
+        if (this._layout && (this._layout !== 'sparse' && this._layout !== 'sparse.coo')) {
             throw new Error("Tensor layout '" + this._layout + "' is not supported.");
         }
         const dataType = this._type.dataType;
         const context = {};
-        context.layout = this._layout;
+        context.encoding = this._encoding;
         context.dimensions = this._type.shape.dimensions.map((value) => !Number.isInteger(value) && value.toNumber ? value.toNumber() : value);
         context.dataType = dataType;
         const size = context.dimensions.reduce((a, b) => a * b, 1);
         switch (this._layout) {
-            case '<':
-            case '>': {
-                context.data = (this._data instanceof Uint8Array || this._data instanceof Int8Array) ? this._data : this._data.peek();
-                context.view = new DataView(context.data.buffer, context.data.byteOffset, context.data.byteLength);
-                if (view.Tensor.dataTypes.has(dataType)) {
-                    context.itemsize = view.Tensor.dataTypes.get(dataType);
-                    if (context.data.length < (context.itemsize * size)) {
-                        throw new Error('Invalid tensor data size.');
-                    }
-                } else if (dataType.startsWith('uint') && !isNaN(parseInt(dataType.substring(4), 10))) {
-                    context.dataType = 'uint';
-                    context.bits = parseInt(dataType.substring(4), 10);
-                    context.itemsize = 1;
-                } else if (dataType.startsWith('int') && !isNaN(parseInt(dataType.substring(3), 10))) {
-                    context.dataType = 'int';
-                    context.bits = parseInt(dataType.substring(3), 10);
-                    context.itemsize = 1;
-                } else {
-                    throw new Error("Tensor data type '" + dataType + "' is not implemented.");
-                }
-                break;
-            }
-            case '|': {
-                context.data = this._values;
-                if (!view.Tensor.dataTypes.has(dataType) && dataType !== 'string' && dataType !== 'object') {
-                    throw new Error("Tensor data type '" + dataType + "' is not implemented.");
-                }
-                if (size !== this._values.length) {
-                    throw new Error('Invalid tensor data length.');
-                }
-                break;
-            }
             case 'sparse': {
                 const indices = new view.Tensor(this._indices).value;
                 const values = new view.Tensor(this._values).value;
                 context.data = this._decodeSparse(dataType, context.dimensions, indices, values);
-                context.layout = '|';
+                context.encoding = '|';
                 break;
             }
             case 'sparse.coo': {
@@ -3496,11 +3474,47 @@ view.Tensor = class {
                     }
                 }
                 context.data = this._decodeSparse(dataType, context.dimensions, indices, values);
-                context.layout = '|';
+                context.encoding = '|';
                 break;
             }
             default: {
-                throw new view.Tensor("Unsupported tensor layout '" + this._layout + "'.");
+                switch (this._encoding) {
+                    case '<':
+                    case '>': {
+                        context.data = (this._data instanceof Uint8Array || this._data instanceof Int8Array) ? this._data : this._data.peek();
+                        context.view = new DataView(context.data.buffer, context.data.byteOffset, context.data.byteLength);
+                        if (view.Tensor.dataTypes.has(dataType)) {
+                            context.itemsize = view.Tensor.dataTypes.get(dataType);
+                            if (context.data.length < (context.itemsize * size)) {
+                                throw new Error('Invalid tensor data size.');
+                            }
+                        } else if (dataType.startsWith('uint') && !isNaN(parseInt(dataType.substring(4), 10))) {
+                            context.dataType = 'uint';
+                            context.bits = parseInt(dataType.substring(4), 10);
+                            context.itemsize = 1;
+                        } else if (dataType.startsWith('int') && !isNaN(parseInt(dataType.substring(3), 10))) {
+                            context.dataType = 'int';
+                            context.bits = parseInt(dataType.substring(3), 10);
+                            context.itemsize = 1;
+                        } else {
+                            throw new Error("Tensor data type '" + dataType + "' is not implemented.");
+                        }
+                        break;
+                    }
+                    case '|': {
+                        context.data = this._values;
+                        if (!view.Tensor.dataTypes.has(dataType) && dataType !== 'string' && dataType !== 'object') {
+                            throw new Error("Tensor data type '" + dataType + "' is not implemented.");
+                        }
+                        if (size !== this._values.length) {
+                            throw new Error('Invalid tensor data length.');
+                        }
+                        break;
+                    }
+                    default: {
+                        throw new view.Tensor("Unsupported tensor encoding '" + this._encoding + "'.");
+                    }
+                }
             }
         }
         context.index = 0;
@@ -5106,7 +5120,7 @@ view.ModelFactoryService = class {
         this._extensions = new Set([ '.zip', '.tar', '.tar.gz', '.tgz', '.gz' ]);
         this._factories = [];
         this.register('./server', [ '.netron']);
-        this.register('./pytorch', [ '.pt', '.pth', '.ptl', '.pt1', '.pyt', '.pyth', '.pkl', '.pickle', '.h5', '.t7', '.model', '.dms', '.tar', '.ckpt', '.chkpt', '.tckpt', '.bin', '.pb', '.zip', '.nn', '.torchmodel', '.torchscript', '.pytorch', '.ot', '.params', '.trt', '.ff', '.ptmf' ], [ '.model' ]);
+        this.register('./pytorch', [ '.pt', '.pth', '.ptl', '.pt1', '.pyt', '.pyth', '.pkl', '.pickle', '.h5', '.t7', '.model', '.dms', '.tar', '.ckpt', '.chkpt', '.tckpt', '.bin', '.pb', '.zip', '.nn', '.torchmodel', '.torchscript', '.pytorch', '.ot', '.params', '.trt', '.ff', '.ptmf', '.jit' ], [ '.model' ]);
         this.register('./onnx', [ '.onnx', '.onn', '.pb', '.onnxtxt', '.pbtxt', '.prototxt', '.txt', '.model', '.pt', '.pth', '.pkl', '.ort', '.ort.onnx', 'onnxmodel', 'ngf', 'json' ]);
         this.register('./mxnet', [ '.json', '.params' ], [ '.mar']);
         this.register('./coreml', [ '.mlmodel', '.bin', 'manifest.json', 'metadata.json', 'featuredescriptions.json', '.pb' ], [ '.mlpackage' ]);
@@ -5123,7 +5137,7 @@ view.ModelFactoryService = class {
         this.register('./lasagne', [ '.pkl', '.pickle', '.joblib', '.model', '.pkl.z', '.joblib.z' ]);
         this.register('./lightgbm', [ '.txt', '.pkl', '.model' ]);
         this.register('./keras', [ '.h5', '.hd5', '.hdf5', '.keras', '.json', '.cfg', '.model', '.pb', '.pth', '.weights', '.pkl', '.lite', '.tflite', '.ckpt' ], [ '.zip' ]);
-        this.register('./sklearn', [ '.pkl', '.pickle', '.joblib', '.model', '.meta', '.pb', '.pt', '.h5', '.pkl.z', '.joblib.z' ]);
+        this.register('./sklearn', [ '.pkl', '.pickle', '.joblib', '.model', '.meta', '.pb', '.pt', '.h5', '.pkl.z', '.joblib.z', '.pickle.dat' ]);
         this.register('./megengine', [ '.tm', '.mge' ]);
         this.register('./pickle', [ '.pkl', '.pickle', '.joblib', '.model', '.meta', '.pb', '.pt', '.h5', '.pkl.z', '.joblib.z', '.pdstates', '.mge' ]);
         this.register('./cntk', [ '.model', '.cntk', '.cmf', '.dnn' ]);
@@ -5159,6 +5173,7 @@ view.ModelFactoryService = class {
         this.register('./mlir', [ '.mlir']);
         this.register('./sentencepiece', [ '.model' ]);
         this.register('./hailo', [ '.hn', '.har' ]);
+        this.register('./nnc', [ '.nnc' ]);
         this.register('./safetensors', [ '.safetensors' ]);
     }
 
@@ -5351,8 +5366,7 @@ view.ModelFactoryService = class {
                 const file_identifier = tags.get('file_identifier');
                 const formats = [
                     { name: 'onnxruntime.experimental.fbs.InferenceSession data', identifier: 'ORTM' },
-                    { name: 'tflite.Model data', identifier: 'TFL3' },
-                    { name: 'FlatBuffers ENNC data', identifier: 'ENNC' },
+                    { name: 'tflite.Model data', identifier: 'TFL3' }
                 ];
                 for (const format of formats) {
                     if (file_identifier === format.identifier) {
diff --git a/source/xmodel.js b/source/xmodel.js
index 20bdd480be8..342d01d5a20 100644
--- a/source/xmodel.js
+++ b/source/xmodel.js
@@ -101,19 +101,13 @@ xmodel.Value = class {
             if (tensor && tensor.tensor_attr && tensor.data_type) {
                 if (initializer) {
                     this.initializer = new xmodel.Tensor(node);
+                    this.type = this.initializer.type;
                 } else {
-                    this._type = new xmodel.TensorType(tensor);
+                    this.type = new xmodel.TensorType(tensor);
                 }
             }
         }
     }
-
-    get type() {
-        if (this.initializer) {
-            return this.initializer.type;
-        }
-        return this._type;
-    }
 };
 
 xmodel.Node = class {
@@ -151,7 +145,8 @@ xmodel.Node = class {
                     this.chain.push(new xmodel.Node(metadata, { op_type: activation }, arg));
                     continue;
                 }
-                this.attributes.push(new xmodel.Attribute(metadata.attribute(this._type, name), name, value));
+                const attribute = new xmodel.Attribute(metadata.attribute(this.type, name), name, value);
+                this.attributes.push(attribute);
             }
         }
         if (op_node.args) {
@@ -174,20 +169,16 @@ xmodel.Attribute = class {
         this.value = attribute.value;
         if (metadata) {
             if (metadata.default !== undefined) {
-                if (metadata.default === this._value) {
-                    this._visible = false;
+                if (metadata.default === this.value) {
+                    this.visible = false;
                 }
-                if (Array.isArray(metadata.default) && Array.isArray(this._value) &&
-                    metadata.default.length === this._value.length && metadata.default.every((value, index) => value === this._value[index])) {
-                    this._visible = false;
+                if (Array.isArray(metadata.default) && Array.isArray(this.value) &&
+                    metadata.default.length === this.value.length && metadata.default.every((value, index) => value === this.value[index])) {
+                    this.visible = false;
                 }
             }
         }
     }
-
-    get visible() {
-        return this._visible == false ? false : true;
-    }
 };
 
 xmodel.TensorType = class {
@@ -253,7 +244,7 @@ xmodel.Tensor = class {
         if (node.op_attr && node.op_attr.data) {
             const data = node.op_attr.data;
             if (data.bytes_value && data.bytes_value.value) {
-                this.layout = '<';
+                this.encoding = '<';
                 this.values = data.bytes_value.value;
             }
         }
@@ -270,6 +261,7 @@ xmodel.Utility = class {
             case 'bool': return { type: 'boolean', value: value };
             case 'int32': return { type: 'int32', value: value };
             case 'int32_vec': return { type: 'int32[]', value: value.value };
+            case 'uint32_vec': return { type: 'uint32[]', value: value.value };
             case 'int64': return { type: 'int64', value: value };
             case 'uint64': return { type: 'uint64', value: value };
             case 'float': return { type: 'float32', value: value };
diff --git a/test/models.js b/test/models.js
index ecd11e42053..5c3e14cbba8 100755
--- a/test/models.js
+++ b/test/models.js
@@ -550,9 +550,9 @@ class Target {
         }
         if (this.assert) {
             for (const assert of this.assert) {
-                const parts = assert.split('=').map((item) => item.trim());
+                const parts = assert.split('==').map((item) => item.trim());
                 const properties = parts[0].split('.');
-                const value = parts[1];
+                const value = JSON.parse(parts[1].replace(/\s*'|'\s*/g, '"'));
                 let context = { model: this.model };
                 while (properties.length) {
                     const property = properties.shift();
@@ -571,7 +571,7 @@ class Target {
                     }
                     throw new Error("Invalid property path: '" + parts[0]);
                 }
-                if (context !== value.toString()) {
+                if (context !== value) {
                     throw new Error("Invalid '" + context.toString() + "' != '" + assert + "'.");
                 }
             }
@@ -592,7 +592,10 @@ class Target {
                 if (value.initializer) {
                     value.initializer.type.toString();
                     const tensor = new view.Tensor(value.initializer);
-                    if (tensor.layout !== '<' && tensor.layout !== '>' && tensor.layout !== '|' && tensor.layout !== 'sparse' && tensor.layout !== 'sparse.coo') {
+                    if (tensor.encoding !== '<' && tensor.encoding !== '>' && tensor.encoding !== '|') {
+                        throw new Error("Tensor encoding '" + tensor.encoding + "' is not implemented.");
+                    }
+                    if (tensor.layout && (tensor.layout !== 'sparse' && tensor.layout !== 'sparse.coo')) {
                         throw new Error("Tensor layout '" + tensor.layout + "' is not implemented.");
                     }
                     if (!tensor.empty) {
diff --git a/test/models.json b/test/models.json
index 90a6e907adc..e15fb0cbfed 100644
--- a/test/models.json
+++ b/test/models.json
@@ -1622,6 +1622,13 @@
     "format":   "Core ML v3",
     "link":     "https://developer.apple.com/machine-learning/models"
   },
+  {
+    "type":     "coreml",
+    "target":   "yolov8n.mlpackage,yolov8n.mlpackage/Manifest.json,yolov8n.mlpackage/Data/com.apple.CoreML/model.mlmodel,yolov8n.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
+    "source":   "https://github.com/lutzroeder/netron/files/12459452/yolov8n.mlpackage.zip[.,yolov8n.mlpackage/Manifest.json,yolov8n.mlpackage/Data/com.apple.CoreML/model.mlmodel,yolov8n.mlpackage/Data/com.apple.CoreML/weights/weight.bin]",
+    "format":   "Core ML Package v5",
+    "link":     "https://github.com/lutzroeder/netron/issues/1145"
+  },
   {
     "type":     "darknet",
     "target":   "alexnet.cfg",
@@ -2135,6 +2142,62 @@
     "format":   "Keras v2.4.0",
     "link":     "https://github.com/lutzroeder/netron/issues/57"
   },
+  {
+    "type":     "keras",
+    "target":   "issue326.json",
+    "source":   "https://github.com/lutzroeder/netron/files/12449721/issue326.json.zip[issue326.json]",
+    "format":   "Keras v2.2.4-tf",
+    "link":     "https://github.com/lutzroeder/netron/issues/326"
+  },
+  {
+    "type":     "keras",
+    "target":   "issue428.h5",
+    "source":   "https://github.com/lutzroeder/netron/files/12449731/issue428.h5.zip[issue428.h5]",
+    "format":   "Keras v2.2.4-tf",
+    "link":     "https://github.com/lutzroeder/netron/issues/428"
+  },
+  {
+    "type":     "keras",
+    "target":   "issue435.h5",
+    "source":   "https://github.com/lutzroeder/netron/files/12449734/issue435.h5.zip[issue435.h5]",
+    "format":   "Keras v2.2.4",
+    "link":     "https://github.com/lutzroeder/netron/issues/435"
+  },
+  {
+    "type":     "keras",
+    "target":   "issue553.h5",
+    "source":   "https://github.com/lutzroeder/netron/files/12449758/issue553.h5.zip[issue553.h5]",
+    "format":   "Keras v2.2.4-tf",
+    "link":     "https://github.com/lutzroeder/netron/issues/553"
+  },
+  {
+    "type":     "keras",
+    "target":   "issue855.h5",
+    "source":   "https://github.com/lutzroeder/netron/files/12449745/issue855.h5.zip[issue855.h5]",
+    "format":   "Keras v2.7.0",
+    "link":     "https://github.com/lutzroeder/netron/issues/855"
+  },
+  {
+    "type":     "keras",
+    "target":   "issue1142.1.keras",
+    "source":   "https://github.com/lutzroeder/netron/files/12407995/issue1142.1.keras.zip[issue1142.1.keras]",
+    "format":   "Keras v2.13.1",
+    "link":     "https://github.com/lutzroeder/netron/issues/1141"
+  },
+  {
+    "type":     "keras",
+    "target":   "issue1142.2.keras",
+    "source":   "https://github.com/lutzroeder/netron/files/12407996/issue1142.2.keras.zip[issue1142.2.keras]",
+    "format":   "Keras v2.13.1",
+    "link":     "https://github.com/lutzroeder/netron/issues/1141"
+  },
+  {
+    "type":     "keras",
+    "target":   "InceptionV3.h5.zip",
+    "source":   "https://github.com/lutzroeder/netron/files/6098151/InceptionV3.h5.zip",
+    "format":   "Keras v2.4.0",
+    "link":     "https://github.com/lutzroeder/netron/issues/57"
+  },
   {
     "type":     "keras",
     "target":   "lstm_seq2seq.h5",
@@ -2246,41 +2309,6 @@
     "format":   "Keras Weights v2.3.1",
     "link":     "https://github.com/lutzroeder/netron/issues/428"
   },
-  {
-    "type":     "keras",
-    "target":   "netron_issue_326.json",
-    "source":   "https://github.com/lutzroeder/netron/files/3563087/netron_issue_326.zip[netron_issue_326.json]",
-    "format":   "Keras v2.2.4-tf",
-    "link":     "https://github.com/lutzroeder/netron/issues/326"
-  },
-  {
-    "type":     "keras",
-    "target":   "netron_issue_428.h5",
-    "source":   "https://github.com/lutzroeder/netron/files/4221535/netron_issue_428.zip[netron_issue_428.h5]",
-    "format":   "Keras v2.2.4-tf",
-    "link":     "https://github.com/lutzroeder/netron/issues/428"
-  },
-  {
-    "type":     "keras",
-    "target":   "netron_issue_435.h5",
-    "source":   "https://github.com/lutzroeder/netron/files/4269953/netron_issue_435.zip[netron_issue_435.h5]",
-    "format":   "Keras v2.2.4",
-    "link":     "https://github.com/lutzroeder/netron/issues/435"
-  },
-  {
-    "type":     "keras",
-    "target":   "netron_issue_553.h5",
-    "source":   "https://github.com/lutzroeder/netron/files/4979486/netron_issue_553.zip[netron_issue_553.h5]",
-    "format":   "Keras v2.2.4-tf",
-    "link":     "https://github.com/lutzroeder/netron/issues/553"
-  },
-  {
-    "type":     "keras",
-    "target":   "netron_issue_855.h5",
-    "source":   "https://github.com/lutzroeder/netron/files/7729736/netron_issue_855.h5.zip[netron_issue_855.h5]",
-    "format":   "Keras v2.7.0",
-    "link":     "https://github.com/lutzroeder/netron/issues/855"
-  },
   {
     "type":     "keras",
     "target":   "nietzsche.h5",
@@ -3091,7 +3119,7 @@
     "target":   "centerface.param,centerface.bin",
     "source":   "https://raw.githubusercontent.com/MirrorYuChen/ncnn_example/798f64b7d5f0b883e05cb994258d43658b0661b6/models/centerface.param,https://raw.githubusercontent.com/MirrorYuChen/ncnn_example/798f64b7d5f0b883e05cb994258d43658b0661b6/models/centerface.bin",
     "format":   "ncnn",
-    "assert":   [ "model.graphs[0].nodes[0].type.name=Convolution" ],
+    "assert":   [ "model.graphs[0].nodes[0].type.name == 'Convolution'" ],
     "link":     "https://github.com/MirrorYuChen/ncnn_example"
   },
   {
@@ -3155,7 +3183,7 @@
     "target":   "MobileNetSSD_deploy.param.bin,MobileNetSSD_deploy.bin",
     "source":   "https://raw.githubusercontent.com/chehongshu/ncnnforandroid_objectiondetection_Mobilenetssd/master/MobileNetSSD_demo/app/src/main/assets/MobileNetSSD_deploy.param.bin,https://raw.githubusercontent.com/chehongshu/ncnnforandroid_objectiondetection_Mobilenetssd/master/MobileNetSSD_demo/app/src/main/assets/MobileNetSSD_deploy.bin",
     "format":   "ncnn",
-    "assert":   [ "model.graphs[0].nodes[1].type.name=Convolution" ],
+    "assert":   [ "model.graphs[0].nodes[1].type.name == 'Convolution'" ],
     "link":     "https://github.com/chehongshu/ncnnforandroid_objectiondetection_Mobilenetssd"
   },
   {
@@ -3299,6 +3327,13 @@
     "error":    "NNEF v1.0 support not implemented.",
     "link":     "https://github.com/lutzroeder/netron/issues/992"
   },
+  {
+    "type":     "nnc",
+    "target":   "sm_uint8_fence.nnc",
+    "source":   "https://github.com/lutzroeder/netron/files/12446059/sm_uint8_fence.nnc.zip[sm_uint8_fence.nnc]",
+    "error":    "File contains undocumented NNC data.",
+    "link":     "https://github.com/lutzroeder/netron/issues/1144"
+  },
   {
     "type":     "numpy",
     "target":   "cifar10-1w-1a.npz",
@@ -3497,7 +3532,7 @@
     "target":   "denotation_Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_255.onnx",
     "source":   "https://github.com/lutzroeder/netron/files/2587943/onnx_denotation_models.zip[denotation_Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_255.onnx]",
     "format":   "ONNX v3",
-    "assert":   [ "model.graphs[0].nodes[0].outputs[0].value[0].type.denotation = Image(Bgr8,SRGB,NominalRange_0_255)" ],
+    "assert":   [ "model.graphs[0].nodes[0].outputs[0].value[0].type.denotation == 'Image(Bgr8,SRGB,NominalRange_0_255)'" ],
     "link":     "https://github.com/lutzroeder/netron/issues/183"
   },
   {
@@ -3857,9 +3892,10 @@
   {
     "type":     "onnx",
     "target":   "sparse_initializer_as_output.json",
-    "source":   "https://github.com/lutzroeder/netron/files/12329156/sparse_initializer_as_output.json.zip[sparse_initializer_as_output.json]",
+    "source":   "https://github.com/lutzroeder/netron/files/12444489/sparse_initializer_as_output.json.zip[sparse_initializer_as_output.json]",
     "format":   "ONNX JSON v7",
-    "link":     "https://github.com/lutzroeder/netron/issues/6"
+    "assert":   [ "model.graphs[0].outputs[0].value[0].type.layout == 'sparse'" ],
+    "link":     "https://github.com/lutzroeder/netron/issues/741"
   },
   {
     "type":     "onnx",
@@ -3868,6 +3904,14 @@
     "format":   "ONNX v7",
     "link":     "https://github.com/lutzroeder/netron/issues/741"
   },
+  {
+    "type":     "onnx",
+    "target":   "sparse_to_dense_matmul.onnx",
+    "source":   "https://github.com/lutzroeder/netron/files/12444490/sparse_to_dense_matmul.onnx.zip[sparse_to_dense_matmul.onnx]",
+    "format":   "ONNX v7",
+    "assert":   [ "model.graphs[0].nodes[0].inputs[0].value[0].type.layout == 'sparse'" ],
+    "link":     "https://github.com/lutzroeder/netron/issues/741"
+  },
   {
     "type":     "onnx",
     "target":   "squeezenet.onnx",
@@ -5091,6 +5135,13 @@
     "format":   "PyTorch v0.1.10",
     "link":     "https://github.com/HengLan/SiamFC-PyTorch"
   },
+  {
+    "type":     "pytorch",
+    "target":   "silero_vad.jit",
+    "source":   "https://github.com/lutzroeder/netron/files/12439374/silero_vad.zip[silero_vad.jit]",
+    "error":    "Supported sampling rates: [8000, 16000] (or multiply of 16000)",
+    "link":     "https://github.com/lutzroeder/netron/issues/1142"
+  },
   {
     "type":     "pytorch",
     "target":   "shufflenet_v2_x1_0.zip.pth",
@@ -5157,6 +5208,7 @@
     "target":   "sparse_coo.pth",
     "source":   "https://github.com/lutzroeder/netron/files/9541426/sparse_coo.pth.zip[sparse_coo.pth]",
     "format":   "PyTorch v1.6",
+    "assert":   [ "model.graphs[0].nodes[0].inputs[0].value[0].type.layout == 'sparse.coo'" ],
     "link":     "https://github.com/lutzroeder/netron/issues/720"
   },
   {
@@ -5436,6 +5488,13 @@
     "format":   "TorchScript v1.5",
     "link":     "https://github.com/lutzroeder/netron/issues/842"
   },
+  {
+    "type":     "rknn",
+    "target":   "100_epoch.rknn",
+    "source":   "https://github.com/lutzroeder/netron/files/12502077/100_epoch.rknn.zip[100_epoch.rknn]",
+    "format":   "RKNN v1.5.2+b642f30c",
+    "link":     "https://github.com/lutzroeder/netron/issues/639"
+  },
   {
     "type":     "rknn",
     "target":   "autopilot.rknn",
@@ -5935,7 +5994,7 @@
     "source":   "https://github.com/lutzroeder/netron/files/5613448/events.out.tfevents.1606692323.b5c8f88cc7ee.58.2.zip[events.out.tfevents.1606692323.b5c8f88cc7ee.58.2]",
     "format":   "TensorFlow Event File v2",
     "producer": "PyTorch",
-    "assert":   [ "model.graphs[0].nodes[0].type.name=aten::_convolution" ],
+    "assert":   [ "model.graphs[0].nodes[0].type.name == 'aten::_convolution'" ],
     "link":     "https://github.com/lutzroeder/netron/issues/638"
   },
   {
@@ -6415,7 +6474,7 @@
     "target":   "densenet.tflite",
     "source":   "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz[densenet/densenet.tflite]",
     "format":   "TensorFlow Lite v3",
-    "assert":   [ "model.graphs[0].nodes[0].type.name=Conv2D" ],
+    "assert":   [ "model.graphs[0].nodes[0].type.name == 'Conv2D'" ],
     "link":     "https://www.tensorflow.org/lite/guide/hosted_models"
   },
   {