diff --git a/source/python.js b/source/python.js
index 95b7696a5e..fb8c3d21ec 100644
--- a/source/python.js
+++ b/source/python.js
@@ -6168,6 +6168,9 @@ python.Execution = class {
             kind() {
                 return this._kind;
             }
+            annotation_str() {
+                return this._annotation_str;
+            }
             equals(/* rhs */) {
                 throw new python.Error(`Not implemented '${this.kind()}'.`);
             }
diff --git a/source/pytorch-metadata.json b/source/pytorch-metadata.json
index 26e5d7f76a..e94bd65e27 100755
--- a/source/pytorch-metadata.json
+++ b/source/pytorch-metadata.json
@@ -32,20009 +32,6444 @@
     ]
   },
   {
-    "name": "_caffe2::BBoxTransform(Tensor rois, Tensor deltas, Tensor im_info, float[] weights, bool apply_scale, bool rotated, bool angle_bound_on, int angle_bound_lo, int angle_bound_hi, float clip_angle_thresh, bool legacy_plus_one) -> (Tensor output_0, Tensor output_1)",
-    "inputs": [
-      { "name": "rois", "type": "Tensor" },
-      { "name": "deltas", "type": "Tensor" },
-      { "name": "im_info", "type": "Tensor" },
-      { "name": "weights", "type": "float32[]" },
-      { "name": "apply_scale", "type": "boolean" },
-      { "name": "rotated", "type": "boolean" },
-      { "name": "angle_bound_on", "type": "boolean" },
-      { "name": "angle_bound_lo", "type": "int64" },
-      { "name": "angle_bound_hi", "type": "int64" },
-      { "name": "clip_angle_thresh", "type": "float32" },
-      { "name": "legacy_plus_one", "type": "boolean" }
-    ],
-    "outputs": [
-      { "name": "output_0", "type": "Tensor" },
-      { "name": "output_1", "type": "Tensor" }
-    ]
+    "name": "_caffe2::BBoxTransform(Tensor rois, Tensor deltas, Tensor im_info, float[] weights, bool apply_scale, bool rotated, bool angle_bound_on, int angle_bound_lo, int angle_bound_hi, float clip_angle_thresh, bool legacy_plus_one) -> (Tensor output_0, Tensor output_1)"
   },
   {
-    "name": "_caffe2::BatchPermutation(Tensor X, Tensor indices) -> Tensor",
-    "inputs": [
-      { "name": "X", "type": "Tensor" },
-      { "name": "indices", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "_caffe2::BatchPermutation(Tensor X, Tensor indices) -> Tensor"
   },
   {
-    "name": "_caffe2::BoxWithNMSLimit(Tensor scores, Tensor boxes, Tensor batch_splits, float score_thresh, float nms, int detections_per_im, bool soft_nms_enabled, str soft_nms_method, float soft_nms_sigma, float soft_nms_min_score_thres, bool rotated, bool cls_agnostic_bbox_reg, bool input_boxes_include_bg_cls, bool output_classes_include_bg_cls, bool legacy_plus_one) -> (Tensor scores, Tensor boxes, Tensor classes, Tensor batch_splits, Tensor keeps, Tensor keeps_size)",
-    "inputs": [
-      { "name": "scores", "type": "Tensor" },
-      { "name": "boxes", "type": "Tensor" },
-      { "name": "batch_splits", "type": "Tensor" },
-      { "name": "score_thresh", "type": "float32" },
-      { "name": "nms", "type": "float32" },
-      { "name": "detections_per_im", "type": "int64" },
-      { "name": "soft_nms_enabled", "type": "boolean" },
-      { "name": "soft_nms_method", "type": "string" },
-      { "name": "soft_nms_sigma", "type": "float32" },
-      { "name": "soft_nms_min_score_thres", "type": "float32" },
-      { "name": "rotated", "type": "boolean" },
-      { "name": "cls_agnostic_bbox_reg", "type": "boolean" },
-      { "name": "input_boxes_include_bg_cls", "type": "boolean" },
-      { "name": "output_classes_include_bg_cls", "type": "boolean" },
-      { "name": "legacy_plus_one", "type": "boolean" }
-    ],
-    "outputs": [
-      { "name": "scores", "type": "Tensor" },
-      { "name": "boxes", "type": "Tensor" },
-      { "name": "classes", "type": "Tensor" },
-      { "name": "batch_splits", "type": "Tensor" },
-      { "name": "keeps", "type": "Tensor" },
-      { "name": "keeps_size", "type": "Tensor" }
-    ]
+    "name": "_caffe2::BoxWithNMSLimit(Tensor scores, Tensor boxes, Tensor batch_splits, float score_thresh, float nms, int detections_per_im, bool soft_nms_enabled, str soft_nms_method, float soft_nms_sigma, float soft_nms_min_score_thres, bool rotated, bool cls_agnostic_bbox_reg, bool input_boxes_include_bg_cls, bool output_classes_include_bg_cls, bool legacy_plus_one) -> (Tensor scores, Tensor boxes, Tensor classes, Tensor batch_splits, Tensor keeps, Tensor keeps_size)"
   },
   {
-    "name": "_caffe2::CollectRpnProposals(Tensor[] input_list, int rpn_max_level, int rpn_min_level, int rpn_post_nms_topN) -> (Tensor rois)",
-    "inputs": [
-      { "name": "input_list", "type": "Tensor[]" },
-      { "name": "rpn_max_level", "type": "int64" },
-      { "name": "rpn_min_level", "type": "int64" },
-      { "name": "rpn_post_nms_topN", "type": "int64" }
-    ],
-    "outputs": [
-      { "name": "rois", "type": "Tensor" }
-    ]
+    "name": "_caffe2::CollectRpnProposals(Tensor[] input_list, int rpn_max_level, int rpn_min_level, int rpn_post_nms_topN) -> (Tensor rois)"
   },
   {
-    "name": "_caffe2::CopyCPUToGPU(Tensor input) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "_caffe2::CopyCPUToGPU(Tensor input) -> Tensor"
   },
   {
-    "name": "_caffe2::CopyGPUToCPU(Tensor input) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "_caffe2::CopyGPUToCPU(Tensor input) -> Tensor"
   },
   {
-    "name": "_caffe2::DistributeFpnProposals(Tensor rois, int roi_canonical_scale, int roi_canonical_level, int roi_max_level, int roi_min_level, bool legacy_plus_one) -> (Tensor rois_fpn2, Tensor rois_fpn3, Tensor rois_fpn4, Tensor rois_fpn5, Tensor rois_idx_restore_int32)",
-    "inputs": [
-      { "name": "rois", "type": "Tensor" },
-      { "name": "roi_canonical_scale", "type": "int64" },
-      { "name": "roi_canonical_level", "type": "int64" },
-      { "name": "roi_max_level", "type": "int64" },
-      { "name": "roi_min_level", "type": "int64" },
-      { "name": "legacy_plus_one", "type": "boolean" }
-    ],
-    "outputs": [
-      { "name": "rois_fpn2", "type": "Tensor" },
-      { "name": "rois_fpn3", "type": "Tensor" },
-      { "name": "rois_fpn4", "type": "Tensor" },
-      { "name": "rois_fpn5", "type": "Tensor" },
-      { "name": "rois_idx_restore_int32", "type": "Tensor" }
-    ]
+    "name": "_caffe2::DistributeFpnProposals(Tensor rois, int roi_canonical_scale, int roi_canonical_level, int roi_max_level, int roi_min_level, bool legacy_plus_one) -> (Tensor rois_fpn2, Tensor rois_fpn3, Tensor rois_fpn4, Tensor rois_fpn5, Tensor rois_idx_restore_int32)"
   },
   {
-    "name": "_caffe2::GenerateProposals(Tensor scores, Tensor bbox_deltas, Tensor im_info, Tensor anchors, float spatial_scale, int pre_nms_topN, int post_nms_topN, float nms_thresh, float min_size, bool angle_bound_on, int angle_bound_lo, int angle_bound_hi, float clip_angle_thresh, bool legacy_plus_one) -> (Tensor output_0, Tensor output_1)",
-    "inputs": [
-      { "name": "scores", "type": "Tensor" },
-      { "name": "bbox_deltas", "type": "Tensor" },
-      { "name": "im_info", "type": "Tensor" },
-      { "name": "anchors", "type": "Tensor" },
-      { "name": "spatial_scale", "type": "float32" },
-      { "name": "pre_nms_topN", "type": "int64" },
-      { "name": "post_nms_topN", "type": "int64" },
-      { "name": "nms_thresh", "type": "float32" },
-      { "name": "min_size", "type": "float32" },
-      { "name": "angle_bound_on", "type": "boolean" },
-      { "name": "angle_bound_lo", "type": "int64" },
-      { "name": "angle_bound_hi", "type": "int64" },
-      { "name": "clip_angle_thresh", "type": "float32" },
-      { "name": "legacy_plus_one", "type": "boolean" }
-    ],
-    "outputs": [
-      { "name": "output_0", "type": "Tensor" },
-      { "name": "output_1", "type": "Tensor" }
-    ]
+    "name": "_caffe2::GenerateProposals(Tensor scores, Tensor bbox_deltas, Tensor im_info, Tensor anchors, float spatial_scale, int pre_nms_topN, int post_nms_topN, float nms_thresh, float min_size, bool angle_bound_on, int angle_bound_lo, int angle_bound_hi, float clip_angle_thresh, bool legacy_plus_one) -> (Tensor output_0, Tensor output_1)"
   },
   {
-    "name": "_caffe2::RoIAlign(Tensor features, Tensor rois, str order, float spatial_scale, int pooled_h, int pooled_w, int sampling_ratio, bool aligned) -> Tensor",
-    "inputs": [
-      { "name": "features", "type": "Tensor" },
-      { "name": "rois", "type": "Tensor" },
-      { "name": "order", "type": "string" },
-      { "name": "spatial_scale", "type": "float32" },
-      { "name": "pooled_h", "type": "int64" },
-      { "name": "pooled_w", "type": "int64" },
-      { "name": "sampling_ratio", "type": "int64" },
-      { "name": "aligned", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "_caffe2::RoIAlign(Tensor features, Tensor rois, str order, float spatial_scale, int pooled_h, int pooled_w, int sampling_ratio, bool aligned) -> Tensor"
   },
   {
-    "name": "aten::Bool.Tensor(Tensor a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "aten::Bool.Tensor(Tensor a) -> bool"
   },
   {
-    "name": "aten::Bool.float(float a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "aten::Bool.float(float a) -> bool"
   },
   {
-    "name": "aten::Bool.int(int a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "aten::Bool.int(int a) -> bool"
   },
   {
-    "name": "aten::Complex.Scalar(Scalar a) -> complex",
-    "inputs": [
-      { "name": "a", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.Scalar(Scalar a) -> complex"
   },
   {
-    "name": "aten::Complex.Tensor_Tensor(Tensor a, Tensor b) -> complex",
-    "inputs": [
-      { "name": "a", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.Tensor_Tensor(Tensor a, Tensor b) -> complex"
   },
   {
-    "name": "aten::Complex.Tensor_bool(Tensor x, bool y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "Tensor" },
-      { "name": "y", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.Tensor_bool(Tensor x, bool y) -> complex"
   },
   {
-    "name": "aten::Complex.Tensor_float(Tensor x, float y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "Tensor" },
-      { "name": "y", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.Tensor_float(Tensor x, float y) -> complex"
   },
   {
-    "name": "aten::Complex.Tensor_int(Tensor x, int y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "Tensor" },
-      { "name": "y", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.Tensor_int(Tensor x, int y) -> complex"
   },
   {
-    "name": "aten::Complex.bool_Tensor(bool x, Tensor y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "boolean" },
-      { "name": "y", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.bool_Tensor(bool x, Tensor y) -> complex"
   },
   {
-    "name": "aten::Complex.bool_bool(bool x, bool y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "boolean" },
-      { "name": "y", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.bool_bool(bool x, bool y) -> complex"
   },
   {
-    "name": "aten::Complex.bool_float(bool x, float y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "boolean" },
-      { "name": "y", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.bool_float(bool x, float y) -> complex"
   },
   {
-    "name": "aten::Complex.bool_int(bool x, int y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "boolean" },
-      { "name": "y", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.bool_int(bool x, int y) -> complex"
   },
   {
-    "name": "aten::Complex.float_Tensor(float x, Tensor y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "float32" },
-      { "name": "y", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.float_Tensor(float x, Tensor y) -> complex"
   },
   {
-    "name": "aten::Complex.float_bool(float x, bool y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "float32" },
-      { "name": "y", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.float_bool(float x, bool y) -> complex"
   },
   {
-    "name": "aten::Complex.float_float(float x, float y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "float32" },
-      { "name": "y", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.float_float(float x, float y) -> complex"
   },
   {
-    "name": "aten::Complex.float_int(float x, int y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "float32" },
-      { "name": "y", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.float_int(float x, int y) -> complex"
   },
   {
-    "name": "aten::Complex.int_Tensor(int x, Tensor y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "int64" },
-      { "name": "y", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.int_Tensor(int x, Tensor y) -> complex"
   },
   {
-    "name": "aten::Complex.int_bool(int x, bool y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "int64" },
-      { "name": "y", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.int_bool(int x, bool y) -> complex"
   },
   {
-    "name": "aten::Complex.int_float(int x, float y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "int64" },
-      { "name": "y", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.int_float(int x, float y) -> complex"
   },
   {
-    "name": "aten::Complex.int_int(int x, int y) -> complex",
-    "inputs": [
-      { "name": "x", "type": "int64" },
-      { "name": "y", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::Complex.int_int(int x, int y) -> complex"
   },
   {
-    "name": "aten::ComplexImplicit(Tensor a) -> complex",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::ComplexImplicit(Tensor a) -> complex"
   },
   {
-    "name": "aten::Float.Scalar(Scalar a) -> float",
-    "inputs": [
-      { "name": "a", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::Float.Scalar(Scalar a) -> float"
   },
   {
-    "name": "aten::Float.Tensor(Tensor a) -> float",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::Float.Tensor(Tensor a) -> float"
   },
   {
-    "name": "aten::Float.bool(bool a) -> float",
-    "inputs": [
-      { "name": "a", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::Float.bool(bool a) -> float"
   },
   {
-    "name": "aten::Float.int(int a) -> float",
-    "inputs": [
-      { "name": "a", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::Float.int(int a) -> float"
   },
   {
-    "name": "aten::Float.str(str a) -> float",
-    "inputs": [
-      { "name": "a", "type": "string" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::Float.str(str a) -> float"
   },
   {
-    "name": "aten::FloatImplicit(Tensor a) -> float",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::FloatImplicit(Tensor a) -> float"
   },
   {
-    "name": "aten::Int.Scalar(Scalar a) -> int",
-    "inputs": [
-      { "name": "a", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::Int.Scalar(Scalar a) -> int"
   },
   {
-    "name": "aten::Int.Tensor(Tensor a) -> int",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::Int.Tensor(Tensor a) -> int"
   },
   {
-    "name": "aten::Int.bool(bool a) -> int",
-    "inputs": [
-      { "name": "a", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::Int.bool(bool a) -> int"
   },
   {
-    "name": "aten::Int.float(float a) -> int",
-    "inputs": [
-      { "name": "a", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::Int.float(float a) -> int"
   },
   {
-    "name": "aten::Int.str(str a) -> int",
-    "inputs": [
-      { "name": "a", "type": "string" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::Int.str(str a) -> int"
   },
   {
-    "name": "aten::IntImplicit(Tensor a) -> int",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::IntImplicit(Tensor a) -> int"
   },
   {
-    "name": "aten::ScalarImplicit(Tensor a) -> Scalar",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Scalar" }
-    ]
+    "name": "aten::ScalarImplicit(Tensor a) -> Scalar"
   },
   {
-    "name": "aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor"
   },
   {
-    "name": "aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor"
   },
   {
-    "name": "aten::__and__.bool(bool a, bool b) -> bool",
-    "inputs": [
-      { "name": "a", "type": "boolean" },
-      { "name": "b", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "aten::__and__.bool(bool a, bool b) -> bool"
   },
   {
-    "name": "aten::__and__.int(int a, int b) -> int",
-    "inputs": [
-      { "name": "a", "type": "int64" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::__and__.int(int a, int b) -> int"
   },
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+    "name": "aten::__xor__.bool(bool a, bool b) -> bool"
   },
   {
-    "name": "aten::_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor",
-    "inputs": [
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-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::__xor__.int(int a, int b) -> int"
   },
   {
-    "name": "aten::_cast_Char(Tensor self, bool non_blocking=False) -> Tensor",
-    "inputs": [
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-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor"
   },
   {
-    "name": "aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor",
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-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor"
   },
   {
-    "name": "aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor",
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-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor"
   },
   {
-    "name": "aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_cast_Short(Tensor self, bool non_blocking=False) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "non_blocking", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)"
   },
   {
-    "name": "aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor",
-    "inputs": [
-      { "name": "x1", "type": "Tensor" },
-      { "name": "x2", "type": "Tensor" },
-      { "name": "p", "type": "float32" },
-      { "name": "compute_mode", "type": "int64?" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)"
   },
   {
-    "name": "aten::_coalesce(Tensor self) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_aminmax(Tensor self) -> (Tensor, Tensor)"
   },
   {
-    "name": "aten::_conj(Tensor(a) self) -> Tensor(a)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor)"
   },
   {
-    "name": "aten::_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor",
-    "category": "Layer",
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-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "SymInt[]", "default": 1 },
-      { "name": "padding", "type": "SymInt[]", "default": 0 },
-      { "name": "dilation", "type": "SymInt[]", "default": 1 },
-      { "name": "transposed", "type": "boolean", "default": false },
-      { "name": "output_padding", "type": "SymInt[]", "default": 0 },
-      { "name": "groups", "type": "SymInt", "default": 1 },
-      { "name": "benchmark", "type": "boolean", "visible": false },
-      { "name": "deterministic", "type": "boolean", "visible": false },
-      { "name": "cudnn_enabled", "type": "boolean", "visible": false },
-      { "name": "allow_tf32", "type": "boolean", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_aminmax.dim_out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
   },
   {
-    "name": "aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "SymInt[]", "default": 1 },
-      { "name": "padding", "type": "SymInt[]", "default": 0 },
-      { "name": "dilation", "type": "SymInt[]", "default": 1 },
-      { "name": "transposed", "type": "boolean", "default": false },
-      { "name": "output_padding", "type": "int64[]", "default": 0 },
-      { "name": "groups", "type": "SymInt", "default": 1 },
-      { "name": "benchmark", "type": "boolean", "visible": false },
-      { "name": "deterministic", "type": "boolean", "visible": false },
-      { "name": "cudnn_enabled", "type": "boolean", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_aminmax.out(Tensor self, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
   },
   {
-    "name": "aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "SymInt[]" },
-      { "name": "padding", "type": "string" },
-      { "name": "dilation", "type": "SymInt[]" },
-      { "name": "groups", "type": "SymInt" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_autocast_to_reduced_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled, ScalarType cuda_dtype, ScalarType cpu_dtype) -> Tensor(a)"
   },
   {
-    "name": "aten::_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "log_probs", "type": "Tensor" },
-      { "name": "targets", "type": "Tensor" },
-      { "name": "input_lengths", "type": "int64[]" },
-      { "name": "target_lengths", "type": "int64[]" },
-      { "name": "blank", "type": "int64", "default": 0 },
-      { "name": "zero_infinity", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "log_probs", "type": "Tensor" },
-      { "name": "targets", "type": "Tensor" },
-      { "name": "input_lengths", "type": "Tensor" },
-      { "name": "target_lengths", "type": "Tensor" },
-      { "name": "blank", "type": "int64", "default": 0 },
-      { "name": "zero_infinity", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Char(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_dim_arange(Tensor like, int dim) -> Tensor",
-    "inputs": [
-      { "name": "like", "type": "Tensor" },
-      { "name": "dim", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_fake_quantize_learnable_per_tensor_affine(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "scale", "type": "Tensor" },
-      { "name": "zero_point", "type": "Tensor" },
-      { "name": "quant_min", "type": "int64" },
-      { "name": "quant_max", "type": "int64" },
-      { "name": "grad_factor", "type": "float32", "default": 1.0 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)",
-    "inputs": [
-      { "name": "grad", "type": "Tensor" },
-      { "name": "self", "type": "Tensor" },
-      { "name": "scale", "type": "Tensor" },
-      { "name": "zero_point", "type": "Tensor" },
-      { "name": "quant_min", "type": "int64" },
-      { "name": "quant_max", "type": "int64" },
-      { "name": "grad_factor", "type": "float32", "default": 1.0 }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "scale", "type": "float32" },
-      { "name": "zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_native_batch_norm_legit_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "running_mean", "type": "Tensor" },
-      { "name": "running_var", "type": "Tensor" },
-      { "name": "training", "type": "boolean" },
-      { "name": "momentum", "type": "float32" },
-      { "name": "eps", "type": "float32" }
-    ],
-    "outputs": [
-      { "name": "output", "type": "Tensor" },
-      { "name": "save_mean", "type": "Tensor" },
-      { "name": "save_rstd", "type": "Tensor" },
-      { "name": "running_mean_out", "type": "Tensor" },
-      { "name": "running_var_out", "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "running_mean", "type": "Tensor" },
-      { "name": "running_var", "type": "Tensor" },
-      { "name": "momentum", "type": "float32" },
-      { "name": "eps", "type": "float32" }
-    ],
-    "outputs": [
-      { "name": "output", "type": "Tensor" },
-      { "name": "save_mean", "type": "Tensor" },
-      { "name": "save_rstd", "type": "Tensor" }
-    ]
+    "name": "aten::_cast_Short(Tensor self, bool non_blocking=False) -> Tensor"
   },
   {
-    "name": "aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "running_mean", "type": "Tensor" },
-      { "name": "running_var", "type": "Tensor" },
-      { "name": "momentum", "type": "float32" },
-      { "name": "eps", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor"
   },
   {
-    "name": "aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor)",
-    "category": "Attention",
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-      { "name": "query", "type": "Tensor" },
-      { "name": "key", "type": "Tensor" },
-      { "name": "value", "type": "Tensor" },
-      { "name": "embed_dim", "type": "int64" },
-      { "name": "num_head", "type": "int64" },
-      { "name": "qkv_weight", "type": "Tensor" },
-      { "name": "qkv_bias", "type": "Tensor" },
-      { "name": "proj_weight", "type": "Tensor" },
-      { "name": "proj_bias", "type": "Tensor" },
-      { "name": "mask", "type": "Tensor?", "default": null },
-      { "name": "need_weights", "type": "boolean", "default": true },
-      { "name": "average_attn_weights", "type": "boolean", "default": true },
-      { "name": "mask_type", "type": "int64?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))",
-    "inputs": [
-      { "name": "query", "type": "Tensor" },
-      { "name": "key", "type": "Tensor" },
-      { "name": "value", "type": "Tensor" },
-      { "name": "embed_dim", "type": "int64" },
-      { "name": "num_head", "type": "int64" },
-      { "name": "qkv_weight", "type": "Tensor" },
-      { "name": "qkv_bias", "type": "Tensor" },
-      { "name": "proj_weight", "type": "Tensor" },
-      { "name": "proj_bias", "type": "Tensor" },
-      { "name": "mask", "type": "Tensor?", "default": null },
-      { "name": "need_weights", "type": "boolean", "default": true },
-      { "name": "average_attn_weights", "type": "boolean", "default": true },
-      { "name": "mask_type", "type": "int64?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_coalesce(Tensor self) -> Tensor"
   },
   {
-    "name": "aten::_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor",
-    "inputs": [
-      { "name": "t", "type": "Tensor" },
-      { "name": "mask", "type": "Tensor" },
-      { "name": "mask_check", "type": "boolean", "default": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "lengths", "type": "Tensor" },
-      { "name": "batch_first", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "name": "?", "type": "Tensor" }
-    ]
+    "name": "aten::_conj(Tensor(a) self) -> Tensor(a)"
   },
   {
-    "name": "aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "data", "type": "Tensor" },
-      { "name": "batch_sizes", "type": "Tensor" },
-      { "name": "batch_first", "type": "boolean" },
-      { "name": "padding_value", "type": "Scalar" },
-      { "name": "total_length", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "name": "?", "type": "Tensor" }
-    ]
+    "name": "aten::_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, *, float? scale=None) -> (Tensor output, Tensor log_sumexp, Tensor philox_seed, Tensor philox_offset)",
-    "inputs": [
-      { "name": "query", "type": "Tensor" },
-      { "name": "key", "type": "Tensor" },
-      { "name": "value", "type": "Tensor" },
-      { "name": "attn_bias", "type": "Tensor?" },
-      { "name": "compute_log_sumexp", "type": "boolean" },
-      { "name": "dropout_p", "type": "float32", "default": 0.0 },
-      { "name": "is_causal", "type": "boolean", "default": false },
-      { "name": "scale", "type": "float32?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "name": "output", "type": "Tensor" },
-      { "name": "log_sumexp", "type": "Tensor" },
-      { "name": "philox_seed", "type": "Tensor" },
-      { "name": "philox_offset", "type": "Tensor" }
-    ]
+    "name": "aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::_scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor output, Tensor logsumexp)",
-    "inputs": [
-      { "name": "query", "type": "Tensor" },
-      { "name": "key", "type": "Tensor" },
-      { "name": "value", "type": "Tensor" },
-      { "name": "dropout_p", "type": "float32", "default": 0.0 },
-      { "name": "is_causal", "type": "boolean", "default": false },
-      { "name": "attn_mask", "type": "Tensor?", "default": null, "kwarg_only": true },
-      { "name": "scale", "type": "float32?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "name": "output", "type": "Tensor" },
-      { "name": "logsumexp", "type": "Tensor" }
-    ]
+    "name": "aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_shape_as_tensor(Tensor self) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor"
   },
   {
-    "name": "aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor",
-    "category": "Activation",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "dim", "type": "int64" },
-      { "name": "half_to_float", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)"
   },
   {
-    "name": "aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "dim", "type": "int64" },
-      { "name": "half_to_float", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)"
   },
   {
-    "name": "aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor",
-    "category": "Tensor",
-    "inputs": [
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-      { "name": "values", "type": "Tensor" },
-      { "name": "size", "type": "SymInt[]" },
-      { "name": "dtype", "type": "ScalarType?", "default": null, "kwarg_only": true },
-      { "name": "layout", "type": "Layout?", "default": null, "kwarg_only": true },
-      { "name": "device", "type": "Device?", "default": null, "kwarg_only": true },
-      { "name": "pin_memory", "type": "boolean?", "default": null, "kwarg_only": true },
-      { "name": "is_coalesced", "type": "boolean?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "name": "outputs", "type": "Tensor" }
-    ]
+    "name": "aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
   },
   {
-    "name": "aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" },
-      { "name": "alpha", "type": "Scalar", "default": 1 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
   },
   {
-    "name": "aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "input_gates", "type": "Tensor" },
-      { "name": "hidden_gates", "type": "Tensor" },
-      { "name": "hx", "type": "Tensor" },
-      { "name": "input_bias", "type": "Tensor?", "default": null },
-      { "name": "hidden_bias", "type": "Tensor?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "name": "output2", "type": "Tensor" }
-    ]
+    "name": "aten::_dim_arange(Tensor like, int dim) -> Tensor"
   },
   {
-    "name": "aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor)",
-    "inputs": [
-      { "name": "input_gates", "type": "Tensor" },
-      { "name": "hidden_gates", "type": "Tensor" },
-      { "name": "cx", "type": "Tensor" },
-      { "name": "input_bias", "type": "Tensor?", "default": null },
-      { "name": "hidden_bias", "type": "Tensor?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "name": "output2", "type": "Tensor" },
-      { "name": "output3", "type": "Tensor" }
-    ]
+    "name": "aten::_fake_quantize_learnable_per_tensor_affine(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.) -> Tensor"
   },
   {
-    "name": "aten::_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "dtype", "type": "ScalarType?", "default": null, "kwarg_only": true },
-      { "name": "layout", "type": "Layout?", "default": null, "kwarg_only": true },
-      { "name": "device", "type": "Device?", "default": null, "kwarg_only": true },
-      { "name": "pin_memory", "type": "boolean?", "default": null, "kwarg_only": true },
-      { "name": "non_blocking", "type": "boolean", "default": false, "kwarg_only": true },
-      { "name": "memory_format", "type": "MemoryFormat?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_fake_quantize_learnable_per_tensor_affine.out(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1., *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_transformer_encoder_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None) -> Tensor",
-    "inputs": [
-      { "name": "src", "type": "Tensor" },
-      { "name": "embed_dim", "type": "int64" },
-      { "name": "num_heads", "type": "int64" },
-      { "name": "qkv_weight", "type": "Tensor" },
-      { "name": "qkv_bias", "type": "Tensor" },
-      { "name": "proj_weight", "type": "Tensor" },
-      { "name": "proj_bias", "type": "Tensor" },
-      { "name": "use_gelu", "type": "boolean" },
-      { "name": "norm_first", "type": "boolean" },
-      { "name": "eps", "type": "float32" },
-      { "name": "norm_weight_1", "type": "Tensor" },
-      { "name": "norm_bias_1", "type": "Tensor" },
-      { "name": "norm_weight_2", "type": "Tensor" },
-      { "name": "norm_bias_2", "type": "Tensor" },
-      { "name": "ffn_weight_1", "type": "Tensor" },
-      { "name": "ffn_bias_1", "type": "Tensor" },
-      { "name": "ffn_weight_2", "type": "Tensor" },
-      { "name": "ffn_bias_2", "type": "Tensor" },
-      { "name": "mask", "type": "Tensor?", "default": null },
-      { "name": "mask_type", "type": "int64?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.) -> (Tensor, Tensor, Tensor)"
   },
   {
-    "name": "aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "sorted", "type": "boolean", "default": true },
-      { "name": "return_inverse", "type": "boolean", "default": false },
-      { "name": "return_counts", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor"
   },
   {
-    "name": "aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "size", "type": "SymInt[]" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_make_per_tensor_quantized_tensor.out(Tensor self, float scale, int zero_point, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::_upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "SymInt[2]" },
-      { "name": "align_corners", "type": "boolean" },
-      { "name": "scales_h", "type": "float32?", "default": null },
-      { "name": "scales_w", "type": "float32?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_native_batch_norm_legit_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)",
+    "category": "Normalization"
   },
   {
-    "name": "aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "SymInt[2]" },
-      { "name": "align_corners", "type": "boolean" },
-      { "name": "scales_h", "type": "float32?", "default": null },
-      { "name": "scales_w", "type": "float32?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
+    "category": "Normalization"
   },
   {
-    "name": "aten::_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "output_size", "type": "SymInt[]?" },
-      { "name": "align_corners", "type": "boolean" },
-      { "name": "scale_factors", "type": "float32[]?" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))"
   },
   {
-    "name": "aten::_weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor",
-    "inputs": [
-      { "name": "v", "type": "Tensor" },
-      { "name": "g", "type": "Tensor" },
-      { "name": "dim", "type": "int64", "default": 0 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor)",
+    "category": "Attention"
   },
   {
-    "name": "aten::_weight_norm_differentiable_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "grad_w", "type": "Tensor" },
-      { "name": "saved_v", "type": "Tensor" },
-      { "name": "saved_g", "type": "Tensor" },
-      { "name": "saved_norms", "type": "Tensor" },
-      { "name": "dim", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
   },
   {
-    "name": "aten::_weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "v", "type": "Tensor" },
-      { "name": "g", "type": "Tensor" },
-      { "name": "dim", "type": "int64", "default": 0 }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor"
   },
   {
-    "name": "aten::_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "grad_w", "type": "Tensor" },
-      { "name": "saved_v", "type": "Tensor" },
-      { "name": "saved_g", "type": "Tensor" },
-      { "name": "saved_norms", "type": "Tensor" },
-      { "name": "dim", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::abs(Tensor self) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)"
   },
   {
-    "name": "aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
   },
   {
-    "name": "aten::abs_(Tensor(a!) self) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)"
   },
   {
-    "name": "aten::acos(Tensor self) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0., bool is_causal=False, *, float? scale=None) -> (Tensor output, Tensor log_sumexp, Tensor philox_seed, Tensor philox_offset)"
   },
   {
-    "name": "aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, float dropout_p=0., bool is_causal=False, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor output, Tensor logsumexp)"
   },
   {
-    "name": "aten::acos_(Tensor(a!) self) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_shape_as_tensor(Tensor self) -> Tensor"
   },
   {
-    "name": "aten::acosh(Tensor self) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor",
+    "category": "Activation"
   },
   {
-    "name": "aten::acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::acosh_(Tensor(a!) self) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor",
+    "category": "Tensor"
   },
   {
-    "name": "aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor",
-    "category": "Pool",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "int64[1]", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor"
   },
   {
-    "name": "aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor",
-    "category": "Pool",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "SymInt[2]", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)"
   },
   {
-    "name": "aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)",
-    "category": "Pool",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "SymInt[2]" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
   },
   {
-    "name": "aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor",
-    "category": "Pool",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "SymInt[3]", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor)"
   },
   {
-    "name": "aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "SymInt[3]" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))"
   },
   {
-    "name": "aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)",
-    "category": "Pool",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "int64[1]", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "name": "?", "type": "Tensor" }
-    ]
+    "name": "aten::_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor"
   },
   {
-    "name": "aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)",
-    "category": "Pool",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "int64[2]", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "name": "?", "type": "Tensor" }
-    ]
+    "name": "aten::_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "int64[2]" }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::_transformer_encoder_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None) -> Tensor"
   },
   {
-    "name": "aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)",
-    "category": "Pool",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_size", "type": "int64[3]", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "name": "?", "type": "Tensor" }
-    ]
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+    "name": "aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor"
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-      { "name": "b", "type": "float32" }
-    ],
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-    ]
+    "name": "aten::_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor"
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-      { "name": "b", "type": "int64" }
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+    "name": "aten::_weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor"
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-      { "name": "b", "type": "float32" }
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+    "name": "aten::_weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor)"
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-      { "name": "b", "type": "int64" }
-    ],
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-    ]
+    "name": "aten::_weight_norm_interface.out(Tensor v, Tensor g, int dim=0, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))"
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+    "name": "aten::acos(Tensor self) -> Tensor"
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-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
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-    ]
+    "name": "aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)",
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-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::contiguous(Tensor(a) self, *, MemoryFormat memory_format=0) -> Tensor(a)"
   },
   {
-    "name": "aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)",
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-      { "name": "other", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=[1], SymInt[1] padding=[0], SymInt[1] dilation=[1], SymInt groups=1) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div.Tensor(Tensor self, Tensor other) -> Tensor",
-    "inputs": [
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-      { "name": "other", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=[1], str padding=\"valid\", SymInt[1] dilation=[1], SymInt groups=1) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor",
-    "inputs": [
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-      { "name": "other", "type": "Tensor" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=[1, 1], SymInt[2] padding=[0, 0], SymInt[2] dilation=[1, 1], SymInt groups=1) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div.complex(complex a, complex b) -> complex",
-    "inputs": [
-      { "name": "a", "type": "complex" },
-      { "name": "b", "type": "complex" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=[1, 1], str padding=\"valid\", SymInt[2] dilation=[1, 1], SymInt groups=1) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div.float(float a, float b) -> float",
-    "inputs": [
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-      { "name": "b", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=[1, 1, 1], SymInt[3] padding=[0, 0, 0], SymInt[3] dilation=[1, 1, 1], SymInt groups=1) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div.int(int a, int b) -> float",
-    "inputs": [
-      { "name": "a", "type": "int64" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=[1, 1, 1], str padding=\"valid\", SymInt[3] dilation=[1, 1, 1], SymInt groups=1) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=[1], SymInt[1] padding=[0], SymInt[1] output_padding=[0], SymInt groups=1, SymInt[1] dilation=[1]) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)",
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-      { "name": "other", "type": "Tensor" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=[1, 1], SymInt[2] padding=[0, 0], SymInt[2] output_padding=[0, 0], SymInt groups=1, SymInt[2] dilation=[1, 1]) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=[1, 1, 1], SymInt[3] padding=[0, 0, 0], SymInt[3] output_padding=[0, 0, 0], SymInt groups=1, SymInt[3] dilation=[1, 1, 1]) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Scalar" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor",
+    "category": "Layer"
   },
   {
-    "name": "aten::div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)"
   },
   {
-    "name": "aten::divide.Scalar(Tensor self, Scalar other) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))"
   },
   {
-    "name": "aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Scalar" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)"
   },
   {
-    "name": "aten::divide.Tensor(Tensor self, Tensor other) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))"
   },
   {
-    "name": "aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor"
   },
   {
-    "name": "aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)"
   },
   {
-    "name": "aten::divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::copy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)"
   },
   {
-    "name": "aten::divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Scalar" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::copy_.float(Tensor(a!) self, float other) -> Tensor(a!)"
   },
   {
-    "name": "aten::divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::copy_.int(Tensor(a!) self, int other) -> Tensor(a!)"
   },
   {
-    "name": "aten::divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "other", "type": "Tensor" },
-      { "name": "rounding_mode", "type": "string?", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cos(Tensor self) -> Tensor"
   },
   {
-    "name": "aten::divmod.float(float x, float y) -> (float, float)",
-    "inputs": [
-      { "name": "x", "type": "float32" },
-      { "name": "y", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "float32" },
-      { "type": "float32" }
-    ]
+    "name": "aten::cos.Scalar(Scalar a) -> Scalar"
   },
   {
-    "name": "aten::divmod.int(int x, int y) -> (int, int)",
-    "inputs": [
-      { "name": "x", "type": "int64" },
-      { "name": "y", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "int64" },
-      { "type": "int64" }
-    ]
+    "name": "aten::cos.complex(complex a) -> complex"
   },
   {
-    "name": "aten::dot(Tensor self, Tensor tensor) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "tensor", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cos.float(float a) -> float"
   },
   {
-    "name": "aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "tensor", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cos.int(int a) -> float"
   },
   {
-    "name": "aten::dropout(Tensor input, float p, bool train) -> Tensor",
-    "category": "Dropout",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "p", "type": "float32", "default": 0.5 },
-      { "name": "train", "type": "boolean", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)",
-    "category": "Dropout",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "p", "type": "float32", "default": 0.5 },
-      { "name": "train", "type": "boolean", "visible": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cosh(Tensor self) -> Tensor"
   },
   {
-    "name": "aten::einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor",
-    "inputs": [
-      { "name": "equation", "type": "string" },
-      { "name": "tensors", "type": "Tensor[]" },
-      { "name": "path", "type": "int64[]?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cosh.Scalar(Scalar a) -> Scalar"
   },
   {
-    "name": "aten::einsum.sublist(Tensor a, ...) -> Tensor",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cosh.complex(complex a) -> complex"
   },
   {
-    "name": "aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor",
-    "category": "Activation",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "alpha", "type": "Scalar", "default": 1 },
-      { "name": "scale", "type": "Scalar", "default": 1 },
-      { "name": "input_scale", "type": "Scalar", "default": 1 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cosh.float(float a) -> float"
   },
   {
-    "name": "aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "alpha", "type": "Scalar", "default": 1 },
-      { "name": "scale", "type": "Scalar", "default": 1 },
-      { "name": "input_scale", "type": "Scalar", "default": 1 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cosh.int(int a) -> float"
   },
   {
-    "name": "aten::elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!)",
-    "category": "Activation",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "alpha", "type": "Scalar", "default": 1 },
-      { "name": "scale", "type": "Scalar", "default": 1 },
-      { "name": "input_scale", "type": "Scalar", "default": 1 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor",
-    "category": "Transform",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "indices", "type": "Tensor" },
-      { "name": "padding_idx", "type": "SymInt", "default": -1 },
-      { "name": "scale_grad_by_freq", "type": "boolean", "default": false },
-      { "name": "sparse", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor"
   },
   {
-    "name": "aten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor)",
-    "category": "Transform",
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-      { "name": "indices", "type": "Tensor" },
-      { "name": "offsets", "type": "Tensor" },
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-      { "name": "mode", "type": "int64", "default": 0 },
-      { "name": "sparse", "type": "boolean", "default": false },
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-      { "name": "output2", "type": "Tensor" },
-      { "name": "output3", "type": "Tensor" },
-      { "name": "output4", "type": "Tensor" }
-    ]
+    "name": "aten::count_nonzero(Tensor self, int? dim=None) -> Tensor"
   },
   {
-    "name": "aten::embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor)",
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-      { "name": "offsets", "type": "Tensor" },
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-      { "name": "mode", "type": "int64" },
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-      { "name": "include_last_offset", "type": "boolean" },
-      { "name": "padding_idx", "type": "int64?" }
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-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" },
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "aten::count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor"
   },
   {
-    "name": "aten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)",
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-      { "name": "indices", "type": "Tensor" },
-      { "name": "max_norm", "type": "float32" },
-      { "name": "norm_type", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::count_nonzero.dim_IntList_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)"
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   {
-    "name": "aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor",
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-      { "name": "device", "type": "Device?", "default": null, "kwarg_only": true },
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-      { "name": "memory_format", "type": "MemoryFormat?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::count_nonzero.out(Tensor self, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)"
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   {
-    "name": "aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor",
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+    "name": "aten::format(str self, ...) -> str",
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+    "name": "aten::gather.dimname(Tensor self, str dim, Tensor index, *, bool sparse_grad=False) -> Tensor",
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-    ],
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+    "name": "aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::gt.float_int(float a, int b) -> bool"
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+    "name": "aten::gt.str(str a, str b) -> bool"
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-      { "name": "R", "type": "Tensor" }
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+    "name": "aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
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-      { "name": "R", "type": "Tensor" }
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+    "name": "aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
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+    "name": "aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
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-      { "name": "info", "type": "Tensor" }
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-      { "name": "info", "type": "Tensor" }
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+    "name": "aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)"
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   {
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-      { "name": "unitriangular", "type": "boolean", "default": false, "kwarg_only": true }
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+    "name": "aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)"
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   {
-    "name": "aten::linalg_svd(Tensor A, bool full_matrices=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh)",
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-      { "name": "S", "type": "Tensor" },
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+    "name": "aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
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-      { "name": "S", "type": "Tensor" },
-      { "name": "Vh", "type": "Tensor" }
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+    "name": "aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)"
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-      { "name": "ind", "type": "int64", "default": 2 }
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-    "outputs": [
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-    ]
+    "name": "aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
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-      { "name": "ind", "type": "int64", "default": 2 }
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+    "name": "aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)"
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-    "name": "aten::linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor",
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-    "outputs": [
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+    "name": "aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor"
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+    "name": "aten::hardsigmoid(Tensor self) -> Tensor",
+    "category": "Activation"
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+    "name": "aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::hardsigmoid_(Tensor(a!) self) -> Tensor(a!)",
+    "category": "Activation"
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+    "name": "aten::hardswish(Tensor self) -> Tensor",
+    "category": "Activation"
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   {
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-      { "type": "Tensor" },
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+    "name": "aten::hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::hardswish_(Tensor(a!) self) -> Tensor(a!)",
+    "category": "Activation"
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+    "name": "aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor",
+    "category": "Activation"
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+    "category": "Activation"
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-    ],
-    "outputs": [
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+    "name": "aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor"
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   {
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-    "outputs": [
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+    "name": "aten::hstack(Tensor[] tensors) -> Tensor"
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-    "outputs": [
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+    "name": "aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::linalg_inv(Tensor A) -> Tensor"
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-    "outputs": [
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-    ]
+    "name": "aten::logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10., *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
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-    ]
+    "name": "aten::logspace.Tensor_Tensor(Tensor start, Tensor end, int steps, float base=10., *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
   },
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-    ],
-    "outputs": [
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-    ]
+    "name": "aten::logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10., *, Tensor(a!) out) -> Tensor(a!)"
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-      { "name": "generator", "type": "Generator?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
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-    ]
+    "name": "aten::logspace.out(Scalar start, Scalar end, int steps, float base=10., *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
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-      { "name": "generator", "type": "Generator?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
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-    ]
+    "name": "aten::logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor"
   },
   {
-    "name": "aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)",
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-      { "name": "generator", "type": "Generator?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::logsumexp.names(Tensor self, str[1] dim, bool keepdim=False) -> Tensor"
   },
   {
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-      { "name": "generator", "type": "Generator?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::logsumexp.names_out(Tensor self, str[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
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-      { "name": "generator", "type": "Generator?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor",
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-      { "name": "layout", "type": "Layout?", "default": null, "kwarg_only": true },
-      { "name": "device", "type": "Device?", "default": null, "kwarg_only": true },
-      { "name": "pin_memory", "type": "boolean?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor)",
+    "category": "Layer"
   },
   {
-    "name": "aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)",
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-      { "name": "generator", "type": "Generator?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor)",
+    "category": "Layer"
   },
   {
-    "name": "aten::normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!)",
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-      { "name": "mean", "type": "float32", "default": 0 },
-      { "name": "std", "type": "float32", "default": 1 },
-      { "name": "generator", "type": "Generator?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)",
+    "category": "Layer"
   },
   {
-    "name": "aten::numel(Tensor self) -> int",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "aten::lt(Scalar a, Scalar b) -> bool"
   },
   {
-    "name": "aten::numpy_T(Tensor(a) self) -> Tensor(a)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.Scalar(Tensor self, Scalar other) -> Tensor"
   },
   {
-    "name": "aten::numpy_T.a(Tensor(a) self) -> Tensor(a)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::one_hot(Tensor self, int num_classes=-1) -> Tensor",
-    "inputs": [
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-      { "name": "num_classes", "type": "int64", "default": -1 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.Tensor(Tensor self, Tensor other) -> Tensor"
   },
   {
-    "name": "aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor",
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-      { "name": "layout", "type": "Layout?", "default": null, "kwarg_only": true },
-      { "name": "device", "type": "Device?", "default": null, "kwarg_only": true },
-      { "name": "pin_memory", "type": "boolean?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor",
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-      { "name": "names", "type": "Dimname[]?", "kwarg_only": true },
-      { "name": "dtype", "type": "ScalarType?", "default": null, "kwarg_only": true },
-      { "name": "layout", "type": "Layout?", "default": null, "kwarg_only": true },
-      { "name": "device", "type": "Device?", "default": null, "kwarg_only": true },
-      { "name": "pin_memory", "type": "boolean?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.float(float a, float b) -> bool"
   },
   {
-    "name": "aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "size", "type": "SymInt[]" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.float_int(float a, int b) -> bool"
   },
   {
-    "name": "aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor",
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-      { "name": "dtype", "type": "ScalarType?", "default": null, "kwarg_only": true },
-      { "name": "layout", "type": "Layout?", "default": null, "kwarg_only": true },
-      { "name": "device", "type": "Device?", "default": null, "kwarg_only": true },
-      { "name": "pin_memory", "type": "boolean?", "default": null, "kwarg_only": true },
-      { "name": "memory_format", "type": "MemoryFormat?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.int(int a, int b) -> bool"
   },
   {
-    "name": "aten::outer(Tensor self, Tensor vec2) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "vec2", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.int_float(int a, float b) -> bool"
   },
   {
-    "name": "aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "vec2", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::lt.str(str a, str b) -> bool"
   },
   {
-    "name": "aten::pad(Tensor self, SymInt[] pad, str mode=\"constant\", float? value=None) -> Tensor",
-    "category": "Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "pad", "type": "SymInt[]" },
-      { "name": "mode", "type": "string", "default": "constant" },
-      { "name": "value", "type": "float32?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::manual_seed(int seed) -> ()"
   },
   {
-    "name": "aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0, str padding_side=\"right\") -> Tensor",
-    "inputs": [
-      { "name": "sequences", "type": "Tensor[]" },
-      { "name": "batch_first", "type": "boolean", "default": false },
-      { "name": "padding_value", "type": "float32", "default": 0.0 },
-      { "name": "padding_side", "type": "string", "default": "right" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::manual_seed.generator(Generator(a!) self, int seed) -> Generator(a!)"
   },
   {
-    "name": "aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor",
-    "inputs": [
-      { "name": "x1", "type": "Tensor" },
-      { "name": "x2", "type": "Tensor" },
-      { "name": "p", "type": "float32", "default": 2 },
-      { "name": "eps", "type": "float32", "default": 1e-06 },
-      { "name": "keepdim", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor"
   },
   {
-    "name": "aten::pdist(Tensor self, float p=2) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "p", "type": "float32", "default": 2 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::masked_fill.Scalar_out(Tensor self, Tensor mask, Scalar value, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)",
-    "category": "Shape",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "dims", "type": "int64[]" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor"
+  },
+  {
+    "name": "aten::masked_fill.Tensor_out(Tensor self, Tensor mask, Tensor value, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::masked_fill_.Scalar(Tensor(a!) self, Tensor mask, Scalar value) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::masked_fill_.Tensor(Tensor(a!) self, Tensor mask, Tensor value) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor"
+  },
+  {
+    "name": "aten::masked_scatter.out(Tensor self, Tensor mask, Tensor source, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::masked_scatter_(Tensor(a!) self, Tensor mask, Tensor source) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::masked_select(Tensor self, Tensor mask) -> Tensor"
+  },
+  {
+    "name": "aten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::matmul(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::max(Tensor self) -> Tensor"
+  },
+  {
+    "name": "aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)"
+  },
+  {
+    "name": "aten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)"
+  },
+  {
+    "name": "aten::max.names_dim(Tensor self, str dim, bool keepdim=False) -> (Tensor values, Tensor indices)"
+  },
+  {
+    "name": "aten::max.names_dim_max(Tensor self, str dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)"
+  },
+  {
+    "name": "aten::max.other(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[0], int[1] dilation=[1], bool ceil_mode=False) -> Tensor",
+    "category": "Pool"
+  },
+  {
+    "name": "aten::max_pool1d_with_indices(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[0], int[1] dilation=[1], bool ceil_mode=False) -> (Tensor, Tensor)",
+    "category": "Pool"
+  },
+  {
+    "name": "aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor",
+    "category": "Pool"
+  },
+  {
+    "name": "aten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)",
+    "category": "Pool"
+  },
+  {
+    "name": "aten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))"
+  },
+  {
+    "name": "aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor",
+    "category": "Pool"
+  },
+  {
+    "name": "aten::max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)"
+  },
+  {
+    "name": "aten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))"
+  },
+  {
+    "name": "aten::max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor"
+  },
+  {
+    "name": "aten::max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::max_unpool2d(Tensor self, Tensor indices, SymInt[2] output_size) -> Tensor",
+    "category": "Pool"
+  },
+  {
+    "name": "aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::max_unpool3d(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding) -> Tensor",
+    "category": "Pool"
+  },
+  {
+    "name": "aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::maximum(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor"
+  },
+  {
+    "name": "aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor"
+  },
+  {
+    "name": "aten::mean.dtype_out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mean.names_dim(Tensor self, str[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor"
+  },
+  {
+    "name": "aten::mean.names_out(Tensor self, str[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::median(Tensor self) -> Tensor"
+  },
+  {
+    "name": "aten::median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)"
+  },
+  {
+    "name": "aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)"
+  },
+  {
+    "name": "aten::median.names_dim(Tensor self, str dim, bool keepdim=False) -> (Tensor values, Tensor indices)"
+  },
+  {
+    "name": "aten::median.names_dim_values(Tensor self, str dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)"
+  },
+  {
+    "name": "aten::median.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::meshgrid(Tensor[] tensors) -> Tensor[]",
+    "category": "Tensor"
+  },
+  {
+    "name": "aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[]",
+    "category": "Tensor"
+  },
+  {
+    "name": "aten::min(Tensor self) -> Tensor"
+  },
+  {
+    "name": "aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)"
+  },
+  {
+    "name": "aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)"
+  },
+  {
+    "name": "aten::min.names_dim(Tensor self, str dim, bool keepdim=False) -> (Tensor values, Tensor indices)"
+  },
+  {
+    "name": "aten::min.names_dim_min(Tensor self, str dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)"
+  },
+  {
+    "name": "aten::min.other(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::minimum(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mish(Tensor self) -> Tensor",
+    "category": "Activation"
+  },
+  {
+    "name": "aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mish_(Tensor(a!) self) -> Tensor(a!)",
+    "category": "Activation"
+  },
+  {
+    "name": "aten::mkldnn_reorder_conv2d_weight(Tensor self, SymInt[2] padding=[0, 0], SymInt[2] stride=[1, 1], SymInt[2] dilation=[1, 1], SymInt groups=1, SymInt[]? input_size=None) -> Tensor"
+  },
+  {
+    "name": "aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=[0, 0], SymInt[2] stride=[1, 1], SymInt[2] dilation=[1, 1], SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mm(Tensor self, Tensor mat2) -> Tensor"
+  },
+  {
+    "name": "aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a)"
+  },
+  {
+    "name": "aten::moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)"
+  },
+  {
+    "name": "aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a)"
+  },
+  {
+    "name": "aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)"
+  },
+  {
+    "name": "aten::mse_loss(Tensor self, Tensor target, int reduction=1) -> Tensor"
+  },
+  {
+    "name": "aten::mse_loss.out(Tensor self, Tensor target, int reduction=1, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor"
+  },
+  {
+    "name": "aten::mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mul(Scalar a, Scalar b) -> Scalar"
+  },
+  {
+    "name": "aten::mul.Scalar(Tensor self, Scalar other) -> Tensor"
+  },
+  {
+    "name": "aten::mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mul.Tensor(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::mul.complex(complex a, complex b) -> complex"
+  },
+  {
+    "name": "aten::mul.complex_float(complex a, float b) -> complex"
+  },
+  {
+    "name": "aten::mul.complex_int(complex a, int b) -> complex"
+  },
+  {
+    "name": "aten::mul.float(float a, float b) -> float"
+  },
+  {
+    "name": "aten::mul.float_complex(float a, complex b) -> complex"
+  },
+  {
+    "name": "aten::mul.float_int(float a, int b) -> float"
+  },
+  {
+    "name": "aten::mul.int(int a, int b) -> int"
+  },
+  {
+    "name": "aten::mul.int_complex(int a, complex b) -> complex"
+  },
+  {
+    "name": "aten::mul.int_float(int a, float b) -> float"
+  },
+  {
+    "name": "aten::mul.left_t(t[] l, int n) -> t[]"
+  },
+  {
+    "name": "aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mul.right_(int n, t[] l) -> t[]"
+  },
+  {
+    "name": "aten::mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mul_.t(t[](a!) l, int n) -> t[](a!)"
+  },
+  {
+    "name": "aten::multinomial(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor"
+  },
+  {
+    "name": "aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::multiply.Scalar(Tensor self, Scalar other) -> Tensor"
+  },
+  {
+    "name": "aten::multiply.Tensor(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::multiply_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::multiply_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mv(Tensor self, Tensor vec) -> Tensor"
+  },
+  {
+    "name": "aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mvlgamma(Tensor self, int p) -> Tensor"
+  },
+  {
+    "name": "aten::mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::mvlgamma_(Tensor(a!) self, int p) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor"
+  },
+  {
+    "name": "aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a)"
+  },
+  {
+    "name": "aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a)"
+  },
+  {
+    "name": "aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor"
+  },
+  {
+    "name": "aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)",
+    "category": "Normalization"
+  },
+  {
+    "name": "aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))"
+  },
+  {
+    "name": "aten::ne(Scalar a, Scalar b) -> bool"
+  },
+  {
+    "name": "aten::ne.Scalar(Tensor self, Scalar other) -> Tensor"
+  },
+  {
+    "name": "aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::ne.Tensor(Tensor self, Tensor other) -> Tensor"
+  },
+  {
+    "name": "aten::ne.Tensor_list(Tensor[] a, Tensor[] b) -> bool"
+  },
+  {
+    "name": "aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::ne.bool(bool a, bool b) -> bool"
+  },
+  {
+    "name": "aten::ne.bool_list(bool[] a, bool[] b) -> bool"
+  },
+  {
+    "name": "aten::ne.complex(complex a, complex b) -> bool"
+  },
+  {
+    "name": "aten::ne.complex_float(complex a, float b) -> bool"
+  },
+  {
+    "name": "aten::ne.device(Device a, Device b) -> bool"
+  },
+  {
+    "name": "aten::ne.enum(AnyEnumType a, AnyEnumType b) -> bool"
+  },
+  {
+    "name": "aten::ne.float(float a, float b) -> bool"
+  },
+  {
+    "name": "aten::ne.float_complex(float a, complex b) -> bool"
+  },
+  {
+    "name": "aten::ne.float_int(float a, int b) -> bool"
+  },
+  {
+    "name": "aten::ne.float_list(float[] a, float[] b) -> bool"
+  },
+  {
+    "name": "aten::ne.int(int a, int b) -> bool"
+  },
+  {
+    "name": "aten::ne.int_float(int a, float b) -> bool"
+  },
+  {
+    "name": "aten::ne.int_list(int[] a, int[] b) -> bool"
+  },
+  {
+    "name": "aten::ne.str(str a, str b) -> bool"
+  },
+  {
+    "name": "aten::ne.str_list(str[] a, str[] b) -> bool"
+  },
+  {
+    "name": "aten::neg(Tensor self) -> Tensor"
+  },
+  {
+    "name": "aten::neg.Scalar(Scalar a) -> Scalar"
+  },
+  {
+    "name": "aten::neg.complex(complex a) -> complex"
+  },
+  {
+    "name": "aten::neg.float(float a) -> float"
+  },
+  {
+    "name": "aten::neg.int(int a) -> int"
+  },
+  {
+    "name": "aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::nested_to_padded_tensor(Tensor self, float padding, int[]? output_size=None) -> Tensor"
+  },
+  {
+    "name": "aten::new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
+  },
+  {
+    "name": "aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
+  },
+  {
+    "name": "aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
+  },
+  {
+    "name": "aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
+  },
+  {
+    "name": "aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
+  },
+  {
+    "name": "aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=1, SymInt ignore_index=-100) -> Tensor"
+  },
+  {
+    "name": "aten::nonzero(Tensor self) -> Tensor"
+  },
+  {
+    "name": "aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::nonzero_numpy(Tensor self) -> Tensor[]"
+  },
+  {
+    "name": "aten::norm.Scalar(Tensor self, Scalar p=2) -> Tensor"
+  },
+  {
+    "name": "aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor"
+  },
+  {
+    "name": "aten::norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor"
+  },
+  {
+    "name": "aten::norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor"
+  },
+  {
+    "name": "aten::norm.ScalarOpt_dtype_out(Tensor self, Scalar? p, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::norm.Scalar_out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::norm.names_ScalarOpt_dim(Tensor self, Scalar? p, str[1] dim, bool keepdim=False) -> Tensor"
+  },
+  {
+    "name": "aten::norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, str[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor"
+  },
+  {
+    "name": "aten::norm.names_dtype_out(Tensor self, Scalar? p, str[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::norm.names_out(Tensor self, Scalar? p, str[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor"
+  },
+  {
+    "name": "aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::normal.Tensor_float(Tensor mean, float std=1., *, Generator? generator=None) -> Tensor"
+  },
+  {
+    "name": "aten::normal.Tensor_float_out(Tensor mean, float std=1., *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::pin_memory(Tensor(a) self, Device? device=None) -> Tensor(a)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "device", "type": "Device?", "default": null }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor"
   },
   {
-    "name": "aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "rcond", "type": "float32", "default": 1e-15 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "upscale_factor", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
   },
   {
-    "name": "aten::pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "downscale_factor", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::polar(Tensor abs, Tensor angle) -> Tensor",
-    "inputs": [
-      { "name": "abs", "type": "Tensor" },
-      { "name": "angle", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::normal.out(Tensor self, float mean=0., float std=1., *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "abs", "type": "Tensor" },
-      { "name": "angle", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::normal_(Tensor(a!) self, float mean=0., float std=1., *, Generator? generator=None) -> Tensor(a!)"
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   {
-    "name": "aten::pop.t(t[](a!) self, int idx=-1) -> t(*)",
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-      { "name": "self", "type": "t[]" },
-      { "name": "idx", "type": "int64", "default": -1 }
-    ],
-    "outputs": [
-      { "type": "t" }
-    ]
+    "name": "aten::numel(Tensor self) -> int"
   },
   {
-    "name": "aten::pow.Scalar(Scalar self, Tensor exponent) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Scalar" },
-      { "name": "exponent", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::numpy_T(Tensor(a) self) -> Tensor(a)"
   },
   {
-    "name": "aten::pow.Scalar_Scalar(Scalar a, Scalar b) -> float",
-    "inputs": [
-      { "name": "a", "type": "Scalar" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::numpy_T.a(Tensor(a) self) -> Tensor(a)"
   },
   {
-    "name": "aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Scalar" },
-      { "name": "exponent", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::one_hot(Tensor self, int num_classes=-1) -> Tensor"
   },
   {
-    "name": "aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "exponent", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
   },
   {
-    "name": "aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)",
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-      { "name": "self", "type": "Tensor" },
-      { "name": "exponent", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::ones.names(int[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
   },
   {
-    "name": "aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "exponent", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::ones.names_out(int[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "exponent", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::pow.complex(complex a, complex b) -> complex",
-    "inputs": [
-      { "name": "a", "type": "complex" },
-      { "name": "b", "type": "complex" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor"
   },
   {
-    "name": "aten::pow.complex_float(complex a, float b) -> complex",
-    "inputs": [
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-      { "name": "b", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::ones_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::pow.float(float a, float b) -> float",
-    "inputs": [
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-      { "name": "b", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::outer(Tensor self, Tensor vec2) -> Tensor"
   },
   {
-    "name": "aten::pow.float_complex(float a, complex b) -> complex",
-    "inputs": [
-      { "name": "a", "type": "float32" },
-      { "name": "b", "type": "complex" }
-    ],
-    "outputs": [
-      { "type": "complex" }
-    ]
+    "name": "aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)"
   },
   {
-    "name": "aten::pow.float_int(float a, int b) -> float",
-    "inputs": [
-      { "name": "a", "type": "float32" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "aten::pad(Tensor self, SymInt[] pad, str mode=\"constant\", float? value=None) -> Tensor",
+    "category": "Tensor"
   },
   {
-    "name": "aten::pow.int(int a, int b) -> float",
-    "inputs": [
-      { "name": "a", "type": "int64" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
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-    ]
+    "name": "aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0., str padding_side=\"right\") -> Tensor"
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-      { "name": "b", "type": "float32" }
-    ],
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-    ]
+    "name": "aten::pairwise_distance(Tensor x1, Tensor x2, float p=2., float eps=9.9999999999999995e-07, bool keepdim=False) -> Tensor"
   },
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-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
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-    ]
+    "name": "aten::pdist(Tensor self, float p=2.) -> Tensor"
   },
   {
-    "name": "aten::pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)",
-    "inputs": [
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-      { "name": "exponent", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)",
+    "category": "Shape"
   },
   {
-    "name": "aten::pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)",
-    "inputs": [
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-      { "name": "exponent", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::pin_memory(Tensor(a) self, Device? device=None) -> Tensor(a)"
+  },
+  {
+    "name": "aten::pinverse(Tensor self, float rcond=1.0000000000000001e-15) -> Tensor"
+  },
+  {
+    "name": "aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor"
+  },
+  {
+    "name": "aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor"
+  },
+  {
+    "name": "aten::pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::polar(Tensor abs, Tensor angle) -> Tensor"
+  },
+  {
+    "name": "aten::polar.Scalar_Scalar(Scalar a, Scalar b) -> Scalar"
+  },
+  {
+    "name": "aten::polar.float(float a, float b) -> complex"
+  },
+  {
+    "name": "aten::polar.float_int(float a, int b) -> complex"
+  },
+  {
+    "name": "aten::polar.int(int a, int b) -> complex"
+  },
+  {
+    "name": "aten::polar.int_float(int a, float b) -> complex"
+  },
+  {
+    "name": "aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::pop.Dict_Tensor(Dict(Tensor, t)(a!) self, Tensor key) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_bool(Dict(bool, t)(a!) self, bool key) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_complex(Dict(complex, t)(a!) self, complex key) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_default_Tensor(Dict(Tensor, t)(a!) self, Tensor key, t default_value) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_default_bool(Dict(bool, t)(a!) self, bool key, t default_value) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_default_complex(Dict(complex, t)(a!) self, complex key, t default_value) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_default_float(Dict(float, t)(a!) self, float key, t default_value) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_default_int(Dict(int, t)(a!) self, int key, t default_value) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_default_str(Dict(str, t)(a!) self, str key, t default_value) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_float(Dict(float, t)(a!) self, float key) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_int(Dict(int, t)(a!) self, int key) -> t(*)"
+  },
+  {
+    "name": "aten::pop.Dict_str(Dict(str, t)(a!) self, str key) -> t(*)"
+  },
+  {
+    "name": "aten::pop.t(t[](a!) self, int idx=-1) -> t(*)"
+  },
+  {
+    "name": "aten::pow.Scalar(Scalar self, Tensor exponent) -> Tensor"
+  },
+  {
+    "name": "aten::pow.Scalar_Scalar(Scalar a, Scalar b) -> float"
+  },
+  {
+    "name": "aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor"
+  },
+  {
+    "name": "aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor"
+  },
+  {
+    "name": "aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::pow.complex(complex a, complex b) -> complex"
+  },
+  {
+    "name": "aten::pow.complex_float(complex a, float b) -> complex"
+  },
+  {
+    "name": "aten::pow.float(float a, float b) -> float"
+  },
+  {
+    "name": "aten::pow.float_complex(float a, complex b) -> complex"
+  },
+  {
+    "name": "aten::pow.float_int(float a, int b) -> float"
+  },
+  {
+    "name": "aten::pow.int(int a, int b) -> float"
+  },
+  {
+    "name": "aten::pow.int_float(int a, float b) -> float"
+  },
+  {
+    "name": "aten::pow.int_to_int(int a, int b) -> int"
+  },
+  {
+    "name": "aten::pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)"
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     "name": "aten::prelu(Tensor self, Tensor weight) -> Tensor",
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-      { "name": "weight", "type": "Tensor" }
-    ],
-    "outputs": [
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-    ]
+    "category": "Activation"
   },
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-    "name": "aten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensor",
-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensor"
   },
   {
-    "name": "aten::prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)",
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-    ],
-    "outputs": [
-      { "type": "Tensor" }
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+    "name": "aten::prod.Dimname_out(Tensor self, str dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)"
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-    ],
-    "outputs": [
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-    ]
+    "name": "aten::prod.dim_Dimname(Tensor self, str dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor"
   },
   {
-    "name": "aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor",
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+    "name": "aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor"
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+    "name": "aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)"
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   {
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-      { "name": "accumulate", "type": "boolean", "default": false }
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-    "outputs": [
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-    ]
+    "name": "aten::prod.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)"
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   {
-    "name": "aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor",
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-      { "name": "interpolation", "type": "string", "default": "linear", "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "aten::put_(Tensor(a!) self, Tensor index, Tensor source, bool accumulate=False) -> Tensor(a!)"
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-    "name": "aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)",
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-      { "name": "interpolation", "type": "string", "default": "linear", "kwarg_only": true }
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-    "outputs": [
-      { "type": "Tensor" }
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+    "name": "aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation=\"linear\") -> Tensor"
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   {
-    "name": "aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor",
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+    "name": "aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation=\"linear\") -> Tensor"
+  },
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+    "name": "aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation=\"linear\", Tensor(a!) out) -> Tensor(a!)"
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     "name": "aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor",
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-      { "name": "dtype", "type": "ScalarType" }
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+    "category": "Quantization"
+  },
+  {
+    "name": "aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)"
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   {
     "name": "aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor",
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-      { "name": "dtype", "type": "ScalarType" }
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-    "outputs": [
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+    "category": "Quantization"
+  },
+  {
+    "name": "aten::quantize_per_tensor.out(Tensor self, float scale, int zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)"
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     "name": "aten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor",
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-      { "name": "dtype", "type": "ScalarType" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
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+    "category": "Quantization"
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+  {
+    "name": "aten::quantize_per_tensor.tensor_qparams_out(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)"
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     "name": "aten::quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[]",
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-    "outputs": [
-      { "name": "outputs", "type": "Tensor[]" }
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+    "category": "Quantization"
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+  {
+    "name": "aten::quantize_per_tensor.tensors_out(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype, *, Tensor(a!)[] out) -> ()"
   },
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     "name": "aten::quantize_per_tensor_dynamic(Tensor self, ScalarType dtype, bool reduce_range) -> Tensor",
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-      { "name": "reduce_range", "type": "boolean" }
-    ],
-    "outputs": [
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+    "category": "Quantization"
+  },
+  {
+    "name": "aten::quantize_per_tensor_dynamic.out(Tensor self, ScalarType dtype, bool reduce_range, *, Tensor(a!) out) -> Tensor(a!)"
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   {
     "name": "aten::quantized_gru.data(Tensor data, Tensor batch_sizes, Tensor hx, __torch__.torch.classes.rnn.CellParamsBase[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)",
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-      { "name": "bidirectional", "type": "boolean" }
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-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
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+    "category": "Layer"
   },
   {
     "name": "aten::quantized_gru.data_legacy(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)",
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-      { "type": "Tensor" }
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+    "category": "Layer"
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   {
     "name": "aten::quantized_gru.input(Tensor input, Tensor hx, __torch__.torch.classes.rnn.CellParamsBase[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)",
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-      { "type": "Tensor" }
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+    "category": "Layer"
   },
   {
     "name": "aten::quantized_gru.input_legacy(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)",
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-      { "type": "Tensor" }
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   {
     "name": "aten::quantized_lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, __torch__.torch.classes.rnn.CellParamsBase[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor)",
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-      { "type": "Tensor" },
-      { "type": "Tensor" }
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+    "category": "Layer"
   },
   {
     "name": "aten::quantized_lstm.data_legacy(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor)",
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   {
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-      { "type": "Tensor" },
-      { "type": "Tensor" }
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+    "category": "Layer"
   },
   {
     "name": "aten::quantized_lstm.input_legacy(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor)",
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-    "outputs": [
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-      { "type": "Tensor" }
-    ]
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+    "name": "aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::reciprocal(Tensor self) -> Tensor"
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+    "name": "aten::requires_grad_(Tensor(a!) self, bool requires_grad=True) -> Tensor(a!)"
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+    "name": "aten::resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!)"
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+    "name": "aten::resolve_conj(Tensor(a) self) -> Tensor(a)"
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-    ],
-    "outputs": [
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+    "name": "aten::resolve_neg(Tensor(a) self) -> Tensor(a)"
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-    "outputs": []
+    "name": "aten::reverse.t(t[](a!) self) -> ()"
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+    "name": "aten::rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)"
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+    "category": "Layer"
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+    "name": "aten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor"
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+    "category": "Layer"
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+    "name": "aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)"
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   {
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-    ],
-    "outputs": [
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-    ]
+    "name": "aten::rot90(Tensor self, int k=1, int[] dims=[0, 1]) -> Tensor"
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-      { "name": "decimals", "type": "int64", "kwarg_only": true }
-    ],
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-      { "name": "decimals", "type": "int64", "kwarg_only": true }
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-    "outputs": [
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+    "name": "aten::round(Tensor self) -> Tensor"
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-    "outputs": [
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+    "name": "aten::round.Scalar(Scalar a) -> Scalar"
   },
   {
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-    ],
-    "outputs": [
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+    "name": "aten::round.decimals(Tensor self, *, int decimals) -> Tensor"
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-      { "name": "decimals", "type": "int64", "kwarg_only": true }
-    ],
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+    "name": "aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::round.float(float a) -> float"
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-    ],
-    "outputs": [
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-    ]
+    "name": "aten::round.int(int a) -> float"
   },
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-    ]
+    "name": "aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
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   {
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-    ],
-    "outputs": [
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-    ]
+    "name": "aten::round_(Tensor(a!) self) -> Tensor(a!)"
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-    ]
+    "name": "aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!)"
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+    "name": "aten::rsqrt(Tensor self) -> Tensor"
+  },
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+    "name": "aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::rsqrt_(Tensor(a!) self) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor"
+  },
+  {
+    "name": "aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor"
+  },
+  {
+    "name": "aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
+  },
+  {
+    "name": "aten::scalar_tensor.out(Scalar s, *, Tensor(a!) out) -> Tensor(a!)"
+  },
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+    "category": "Attention"
+  },
+  {
+    "name": "aten::scatter.dimname_src(Tensor self, str dim, Tensor index, Tensor src) -> Tensor"
+  },
+  {
+    "name": "aten::scatter.dimname_value(Tensor self, str dim, Tensor index, Scalar value) -> Tensor"
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+    "category": "Activation"
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+    "category": "Activation"
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+    "name": "aten::sign_(Tensor(a!) self) -> Tensor(a!)"
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+    "name": "aten::signbit(Tensor self) -> Tensor"
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+    "name": "aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
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+    "category": "Activation"
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+    "category": "Activation"
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-      { "name": "self", "type": "Tensor" }
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+    "name": "aten::silu_backward(Tensor grad_output, Tensor self) -> Tensor"
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-    ],
-    "outputs": [
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+    "name": "aten::sin(Tensor self) -> Tensor"
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-    ],
-    "outputs": [
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+    "name": "aten::sin.Scalar(Scalar a) -> Scalar"
   },
   {
-    "name": "aten::sinh(Tensor self) -> Tensor",
-    "inputs": [
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-    ],
-    "outputs": [
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+    "name": "aten::sin.complex(complex a) -> complex"
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-    ],
-    "outputs": [
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+    "name": "aten::sin.float(float a) -> float"
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-    ],
-    "outputs": [
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+    "name": "aten::sin.int(int a) -> float"
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-      { "name": "dim", "type": "Dimname" }
-    ],
-    "outputs": [
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+    "name": "aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
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-    ],
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+    "name": "aten::sinh(Tensor self) -> Tensor"
+  },
+  {
+    "name": "aten::sinh.Scalar(Scalar a) -> Scalar"
+  },
+  {
+    "name": "aten::sinh.complex(complex a) -> complex"
+  },
+  {
+    "name": "aten::sinh.float(float a) -> float"
+  },
+  {
+    "name": "aten::sinh.int(int a) -> float"
+  },
+  {
+    "name": "aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::size(Tensor self) -> int[]"
+  },
+  {
+    "name": "aten::size.Dimname(Tensor self, str dim) -> int"
+  },
+  {
+    "name": "aten::size.int(Tensor self, int dim) -> int"
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+  },
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+    "name": "aten::unsqueeze_copy(Tensor self, int dim) -> Tensor"
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+    "name": "aten::values(Tensor(a) self) -> Tensor(a)"
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+    "name": "aten::values.complex(Dict(complex, t) self) -> t[](*)"
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+    "name": "aten::var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor"
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+    "name": "aten::var.names_dim(Tensor self, str[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor"
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+    "name": "aten::var.names_out(Tensor self, str[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)"
+  },
+  {
+    "name": "aten::var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)"
+  },
+  {
+    "name": "aten::var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)"
+  },
+  {
+    "name": "aten::var_mean.correction_names(Tensor self, str[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)"
+  },
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+    "name": "aten::vdot(Tensor self, Tensor other) -> Tensor"
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+    "name": "aten::view_as_complex(Tensor(a) self) -> Tensor(a)"
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+    "name": "aten::view_as_complex_copy(Tensor self) -> Tensor"
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+    "name": "aten::view_as_real(Tensor(a) self) -> Tensor(a)"
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+    "name": "aten::view_as_real_copy(Tensor self) -> Tensor"
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+    "name": "aten::vstack(Tensor[] tensors) -> Tensor"
   },
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-    "name": "aten::vstack(Tensor[] tensors) -> Tensor",
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+    "name": "aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::wait(Future(t) self) -> t"
   },
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+    "name": "aten::where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor"
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+    "name": "aten::where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor"
   },
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+    "name": "aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor"
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+    "name": "aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)"
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+    "name": "aten::zero_(Tensor(a!) self) -> Tensor(a!)"
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   {
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+    "name": "aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor"
   },
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-    ]
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-    "name": "aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)",
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-    ],
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-      { "type": "Tensor" }
-    ]
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-      { "name": "memory_format", "type": "MemoryFormat?", "default": null, "kwarg_only": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
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-      { "name": "_1", "type": "__torch__.torch.classes.neuron.Model" }
-    ],
-    "outputs": [
-      { "name": "_0", "type": "Tensor" }
-    ]
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-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.xnnpack.Conv2dOpContext" }
-    ]
+    "name": "neuron::forward_v2_1(Tensor[] _0, __torch__.torch.classes.neuron.Model _1) -> (Tensor _0)"
+  },
+  {
+    "name": "prepacked::conv2d_clamp_prepack(Tensor W, Tensor? B, int[2] stride, int[2] padding, int[2] dilation, int groups, Scalar? output_min=None, Scalar? output_max=None) -> __torch__.torch.classes.xnnpack.Conv2dOpContext"
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-    ],
-    "outputs": [
-      { "name": "Y", "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "prepacked::conv2d_transpose_clamp_run(Tensor X, __torch__.torch.classes.xnnpack.TransposeConv2dOpContext W_prepack) -> Tensor Y",
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-      { "name": "W_prepack", "type": "__torch__.torch.classes.xnnpack.TransposeConv2dOpContext" }
-    ],
-    "outputs": [
-      { "name": "Y", "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
-    "name": "prepacked::linear_clamp_prepack(Tensor W, Tensor? B=None, Scalar? output_min=None, Scalar? output_max=None) -> __torch__.torch.classes.xnnpack.LinearOpContext",
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-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.xnnpack.LinearOpContext" }
-    ]
+    "name": "prepacked::linear_clamp_prepack(Tensor W, Tensor? B=None, Scalar? output_min=None, Scalar? output_max=None) -> __torch__.torch.classes.xnnpack.LinearOpContext"
   },
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-      { "name": "b", "type": "Any" }
-    ],
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-      { "type": "Any" }
-    ]
+    "category": "Layer"
   },
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-    "name": "prim::AutogradAllNonZero(...) -> bool",
-    "inputs": [],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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-    "name": "prim::AutogradAllZero(...) -> bool",
-    "inputs": [],
-    "outputs": [
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-    ]
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   },
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-    "name": "prim::AutogradAnyNonZero(...) -> bool",
-    "inputs": [],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "prim::AutogradAllZero(...) -> bool"
   },
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-    "name": "prim::AutogradZero() -> Tensor",
-    "inputs": [],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "prim::AutogradAnyNonZero(...) -> bool"
   },
   {
-    "name": "prim::BroadcastSizes(...) -> int[]",
-    "inputs": [],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "prim::AutogradZero() -> Tensor"
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-    "inputs": [
-      { "name": "enum", "type": "AnyEnumType" }
-    ],
-    "outputs": [
-      { "type": "string" }
-    ]
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-    "inputs": [
-      { "name": "enum", "type": "AnyEnumType" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
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-    "name": "prim::EnumValue.int(AnyEnumType enum) -> int",
-    "inputs": [
-      { "name": "enum", "type": "AnyEnumType" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
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   },
   {
-    "name": "prim::EnumValue.str(AnyEnumType enum) -> str",
-    "inputs": [
-      { "name": "enum", "type": "AnyEnumType" }
-    ],
-    "outputs": [
-      { "type": "string" }
-    ]
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-      { "name": "y", "type": "Any" }
-    ],
-    "outputs": [
-      { "type": "Any" }
-    ]
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-      { "name": "ind", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Any" }
-    ]
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-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
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-    "inputs": [
-      { "name": "a", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
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   {
-    "name": "prim::Print(...) -> ()",
-    "inputs": [],
-    "outputs": []
+    "name": "prim::NumToTensor.Scalar(Scalar a) -> Tensor"
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-    "inputs": [
-      { "name": "msg", "type": "string" },
-      { "name": "cls", "type": "string?", "default": null }
-    ],
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-      { "name": "keepdim", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
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-    "inputs": [
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-      { "name": "index", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "string" }
-    ]
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-      { "name": "i", "type": "int64" }
-    ],
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-    "inputs": [
-      { "name": "tup", "type": "Any" }
-    ],
-    "outputs": []
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   {
-    "name": "prim::Uninitialized() -> Any",
-    "inputs": [],
-    "outputs": [
-      { "type": "Any" }
-    ]
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   {
-    "name": "prim::VarConcat(...) -> Tensor",
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-    "outputs": [
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-    "name": "prim::VarStack(...) -> Tensor",
-    "inputs": [],
-    "outputs": [
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-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
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-    "name": "prim::abs.Scalar(Scalar a) -> Scalar",
-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "Scalar" }
-    ]
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-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
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-    ],
-    "outputs": [
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-    ]
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-    "inputs": [
-      { "name": "a", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
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-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
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-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "Device" }
-    ]
+    "name": "prim::abs.int(int a) -> int"
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-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "prim::data(Tensor(a) a) -> Tensor(a)"
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   {
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-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
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   {
-    "name": "prim::id(AnyClassType? x) -> int",
-    "inputs": [
-      { "name": "x", "type": "AnyClassType?" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "prim::dtype(Tensor a) -> int"
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-    "name": "prim::index(Device self) -> int?",
-    "inputs": [
-      { "name": "self", "type": "Device" }
-    ],
-    "outputs": [
-      { "type": "int64?" }
-    ]
+    "name": "prim::grad(Tensor a) -> Tensor(*)"
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-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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   {
-    "name": "prim::is_mkldnn(Tensor a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "prim::is_maia(Tensor a) -> bool"
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   {
-    "name": "prim::is_mps(Tensor a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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   {
-    "name": "prim::is_mtia(Tensor a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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   },
   {
-    "name": "prim::is_nested(Tensor a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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   {
-    "name": "prim::is_quantized(Tensor a) -> bool",
-    "inputs": [
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "prim::is_mtia(Tensor a) -> bool"
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-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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   {
-    "name": "prim::is_vulkan(Tensor a) -> bool",
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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   {
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-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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   {
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-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
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-    "inputs": [
-      { "name": "to_check", "type": "Any" }
-    ],
-    "outputs": [
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-    ]
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-    ],
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-    ]
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-    ],
-    "outputs": [
-      { "type": "Layout" }
-    ]
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-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Scalar" }
-    ]
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-      { "name": "r", "type": "boolean[]" }
-    ],
-    "outputs": [
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-    ]
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   },
   {
-    "name": "prim::max.float(float a, float b) -> float",
-    "inputs": [
-      { "name": "a", "type": "float32" },
-      { "name": "b", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::max(Scalar a, Scalar b) -> Scalar"
   },
   {
-    "name": "prim::max.float_int(float a, int b) -> float",
-    "inputs": [
-      { "name": "a", "type": "float32" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::max.bool_list(bool[] l, bool[] r) -> bool[]"
   },
   {
-    "name": "prim::max.float_list(float[] l, float[] r) -> float[]",
-    "inputs": [
-      { "name": "l", "type": "float32[]" },
-      { "name": "r", "type": "float32[]" }
-    ],
-    "outputs": [
-      { "type": "float32[]" }
-    ]
+    "name": "prim::max.float(float a, float b) -> float"
   },
   {
-    "name": "prim::max.int(int a, int b) -> int",
-    "inputs": [
-      { "name": "a", "type": "int64" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "prim::max.float_int(float a, int b) -> float"
   },
   {
-    "name": "prim::max.int_float(int a, float b) -> float",
-    "inputs": [
-      { "name": "a", "type": "int64" },
-      { "name": "b", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::max.float_list(float[] l, float[] r) -> float[]"
   },
   {
-    "name": "prim::max.int_list(int[] l, int[] r) -> int[]",
-    "inputs": [
-      { "name": "l", "type": "int64[]" },
-      { "name": "r", "type": "int64[]" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "prim::max.int(int a, int b) -> int"
   },
   {
-    "name": "prim::max.self_bool(bool[] self) -> bool",
-    "inputs": [
-      { "name": "self", "type": "boolean[]" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "prim::max.int_float(int a, float b) -> float"
   },
   {
-    "name": "prim::max.self_float(float[] self) -> float",
-    "inputs": [
-      { "name": "self", "type": "float32[]" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::max.int_list(int[] l, int[] r) -> int[]"
   },
   {
-    "name": "prim::max.self_int(int[] self) -> int",
-    "inputs": [
-      { "name": "self", "type": "int64[]" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "prim::max.self_bool(bool[] self) -> bool"
   },
   {
-    "name": "prim::min(Scalar a, Scalar b) -> Scalar",
-    "inputs": [
-      { "name": "a", "type": "Scalar" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "type": "Scalar" }
-    ]
+    "name": "prim::max.self_float(float[] self) -> float"
   },
   {
-    "name": "prim::min.bool_list(bool[] l, bool[] r) -> bool[]",
-    "inputs": [
-      { "name": "l", "type": "boolean[]" },
-      { "name": "r", "type": "boolean[]" }
-    ],
-    "outputs": [
-      { "type": "boolean[]" }
-    ]
+    "name": "prim::max.self_int(int[] self) -> int"
   },
   {
-    "name": "prim::min.float(float a, float b) -> float",
-    "inputs": [
-      { "name": "a", "type": "float32" },
-      { "name": "b", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::min(Scalar a, Scalar b) -> Scalar"
   },
   {
-    "name": "prim::min.float_int(float a, int b) -> float",
-    "inputs": [
-      { "name": "a", "type": "float32" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::min.bool_list(bool[] l, bool[] r) -> bool[]"
   },
   {
-    "name": "prim::min.float_list(float[] l, float[] r) -> float[]",
-    "inputs": [
-      { "name": "l", "type": "float32[]" },
-      { "name": "r", "type": "float32[]" }
-    ],
-    "outputs": [
-      { "type": "float32[]" }
-    ]
+    "name": "prim::min.float(float a, float b) -> float"
   },
   {
-    "name": "prim::min.int(int a, int b) -> int",
-    "inputs": [
-      { "name": "a", "type": "int64" },
-      { "name": "b", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "prim::min.float_int(float a, int b) -> float"
   },
   {
-    "name": "prim::min.int_float(int a, float b) -> float",
-    "inputs": [
-      { "name": "a", "type": "int64" },
-      { "name": "b", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::min.float_list(float[] l, float[] r) -> float[]"
   },
   {
-    "name": "prim::min.int_list(int[] l, int[] r) -> int[]",
-    "inputs": [
-      { "name": "l", "type": "int64[]" },
-      { "name": "r", "type": "int64[]" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "prim::min.int(int a, int b) -> int"
   },
   {
-    "name": "prim::min.self_bool(bool[] self) -> bool",
-    "inputs": [
-      { "name": "self", "type": "boolean[]" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "prim::min.int_float(int a, float b) -> float"
   },
   {
-    "name": "prim::min.self_float(float[] self) -> float",
-    "inputs": [
-      { "name": "self", "type": "float32[]" }
-    ],
-    "outputs": [
-      { "type": "float32" }
-    ]
+    "name": "prim::min.int_list(int[] l, int[] r) -> int[]"
   },
   {
-    "name": "prim::min.self_int(int[] self) -> int",
-    "inputs": [
-      { "name": "self", "type": "int64[]" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "prim::min.self_bool(bool[] self) -> bool"
   },
   {
-    "name": "prim::name(Tensor a) -> str?",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "string?" }
-    ]
+    "name": "prim::min.self_float(float[] self) -> float"
   },
   {
-    "name": "prim::nbytes(Tensor a) -> int",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "prim::min.self_int(int[] self) -> int"
   },
   {
-    "name": "prim::rangelist(int n) -> int[]",
-    "inputs": [
-      { "name": "n", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "prim::name(Tensor a) -> str?"
   },
   {
-    "name": "prim::requires_grad(Tensor a) -> bool",
-    "inputs": [
-      { "name": "a", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "boolean" }
-    ]
+    "name": "prim::nbytes(Tensor a) -> int"
   },
   {
-    "name": "prim::shape(Tensor self) -> int[]",
-    "inputs": [
-      { "name": "self", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "prim::rangelist(int n) -> int[]"
   },
   {
-    "name": "prim::type(Device self) -> str",
-    "inputs": [
-      { "name": "self", "type": "Device" }
-    ],
-    "outputs": [
-      { "type": "string" }
-    ]
+    "name": "prim::requires_grad(Tensor a) -> bool"
   },
   {
-    "name": "prim::unchecked_cast(t x) -> t",
-    "inputs": [
-      { "name": "x", "type": "t" }
-    ],
-    "outputs": [
-      { "type": "t" }
-    ]
+    "name": "prim::shape(Tensor self) -> int[]"
   },
   {
-    "name": "prim::unchecked_unwrap_optional(t(a)? optional) -> t(a)",
-    "inputs": [
-      { "name": "optional", "type": "t?" }
-    ],
-    "outputs": [
-      { "type": "t" }
-    ]
+    "name": "prim::type(Device self) -> str"
   },
   {
-    "name": "quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "scale", "type": "float32" },
-      { "name": "zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "prim::unchecked_cast(t x) -> t"
   },
   {
-    "name": "quantized::add.Scalar(Tensor qa, Scalar b) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "prim::unchecked_unwrap_optional(t(a)? optional) -> t(a)"
   },
   {
-    "name": "quantized::add.Scalar2(Scalar b, Tensor qa) -> Tensor qc",
-    "inputs": [
-      { "name": "b", "type": "Scalar" },
-      { "name": "qa", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"
   },
   {
-    "name": "quantized::add.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add.Scalar(Tensor qa, Scalar b) -> Tensor qc"
   },
   {
-    "name": "quantized::add.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add.Scalar2(Scalar b, Tensor qa) -> Tensor qc"
   },
   {
-    "name": "quantized::add_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_relu(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "scale", "type": "float32" },
-      { "name": "zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_relu.Scalar(Tensor qa, Scalar b) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_relu.Scalar2(Scalar b, Tensor qa) -> Tensor qc",
-    "inputs": [
-      { "name": "b", "type": "Scalar" },
-      { "name": "qa", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add_relu(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"
+  },
+  {
+    "name": "quantized::add_relu.Scalar(Tensor qa, Scalar b) -> Tensor qc"
+  },
+  {
+    "name": "quantized::add_relu.Scalar2(Scalar b, Tensor qa) -> Tensor qc"
   },
   {
-    "name": "quantized::add_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_relu.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add_relu.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_relu_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add_relu_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_scalar(Tensor qa, Scalar b) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar(Tensor qa, Scalar b) -> Tensor qc"
   },
   {
-    "name": "quantized::add_scalar.Tensor(Tensor qa, Tensor b) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar.Tensor(Tensor qa, Tensor b) -> Tensor qc"
   },
   {
-    "name": "quantized::add_scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_scalar_relu(Tensor qa, Scalar b) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar_relu(Tensor qa, Scalar b) -> Tensor qc"
   },
   {
-    "name": "quantized::add_scalar_relu.Tensor(Tensor qa, Tensor b) -> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar_relu.Tensor(Tensor qa, Tensor b) -> Tensor qc"
   },
   {
-    "name": "quantized::add_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::add_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::add_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::batch_norm(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::batch_norm(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"
   },
   {
     "name": "quantized::batch_norm1d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::batch_norm1d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::batch_norm2d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::batch_norm2d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::batch_norm3d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::batch_norm3d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::batch_norm_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "mean", "type": "Tensor" },
-      { "name": "var", "type": "Tensor" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::cat(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor",
-    "category": "Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor[]" },
-      { "name": "dim", "type": "int64" },
-      { "name": "scale", "type": "float32?" },
-      { "name": "zero_point", "type": "int64?" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Tensor"
   },
   {
     "name": "quantized::cat_relu(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor",
-    "category": "Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor[]" },
-      { "name": "dim", "type": "int64" },
-      { "name": "scale", "type": "float32?" },
-      { "name": "zero_point", "type": "int64?" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Tensor"
   },
   {
     "name": "quantized::celu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1) -> Tensor",
-    "category": "Activation",
-    "inputs": [
-      { "name": "self", "type": "Tensor" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" },
-      { "name": "alpha", "type": "Scalar", "default": 1 }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Activation"
   },
   {
     "name": "quantized::conv1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
-    "name": "quantized::conv1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ]
+    "name": "quantized::conv1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"
   },
   {
     "name": "quantized::conv1d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "quantized::conv2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "quantized::conv2d.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
-    "name": "quantized::conv2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "reduce_range", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::conv2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"
   },
   {
-    "name": "quantized::conv2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "quantized::conv2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"
   },
   {
-    "name": "quantized::conv2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ]
+    "name": "quantized::conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"
   },
   {
     "name": "quantized::conv2d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "quantized::conv2d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
-    "name": "quantized::conv2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "quantized::conv2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"
   },
   {
-    "name": "quantized::conv2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "name": "unpacked_weights", "type": "Tensor" },
-      { "name": "B_origin", "type": "Tensor?" }
-    ]
+    "name": "quantized::conv2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"
   },
   {
-    "name": "quantized::conv2d_unpack_sizes(Any packed_weights) -> (Any)",
-    "inputs": [
-      { "name": "packed_weights", "type": "Any" }
-    ],
-    "outputs": [
-      { "type": "Any" }
-    ]
+    "name": "quantized::conv2d_unpack_sizes(Any packed_weights) -> Any"
   },
   {
     "name": "quantized::conv3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "__torch__.torch.classes.quantized.Conv3dPackedParamsBase" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]", "default": 1 },
-      { "name": "dilation", "type": "int64[]", "default": 0 },
-      { "name": "groups", "type": "int64", "default": 1 },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "quantized::conv3d.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv3dPackedParamsBase" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
-    "name": "quantized::conv3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.quantized.Conv3dPackedParamsBase" }
-    ]
+    "name": "quantized::conv3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"
   },
   {
     "name": "quantized::conv3d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "__torch__.torch.classes.quantized.Conv3dPackedParamsBase" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]", "default": 1 },
-      { "name": "dilation", "type": "int64[]", "default": 0 },
-      { "name": "groups", "type": "int64", "default": 1 },
-      { "name": "output_scale", "type": "float32", "default": 1 },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "quantized::conv3d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Layer",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv3dPackedParamsBase" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
-  },
-  {
-    "name": "quantized::conv_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ]
+    "category": "Layer"
   },
-  {
-    "name": "quantized::conv_transpose1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "output_padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ]
+  {
+    "name": "quantized::conv_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"
   },
   {
-    "name": "quantized::conv_transpose2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::conv_transpose1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"
   },
   {
-    "name": "quantized::conv_transpose2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv_transpose2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"
   },
   {
-    "name": "quantized::conv_transpose2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "packed_weight", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" },
-      { "name": "reduce_range", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::conv_transpose2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv_transpose2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "quantized::conv_transpose2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"
   },
   {
-    "name": "quantized::conv_transpose2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv_transpose2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"
   },
   {
-    "name": "quantized::conv_transpose2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv_transpose2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv_transpose2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "stride", "type": "int64[]" },
-      { "name": "padding", "type": "int64[]" },
-      { "name": "output_padding", "type": "int64[]" },
-      { "name": "dilation", "type": "int64[]" },
-      { "name": "groups", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ]
+    "name": "quantized::conv_transpose2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv_transpose2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64[]" }
-    ]
+    "name": "quantized::conv_transpose2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"
   },
   {
-    "name": "quantized::conv_transpose2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "int64" }
-    ]
+    "name": "quantized::conv_transpose2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"
   },
   {
-    "name": "quantized::conv_transpose2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)",
-    "inputs": [
-      { "name": "packed_weights", "type": "__torch__.torch.classes.quantized.Conv2dPackedParamsBase" }
-    ],
-    "outputs": [
-      { "name": "unpacked_weights", "type": "Tensor" },
-      { "name": "B_origin", "type": "Tensor?" }
-    ]
+    "name": "quantized::conv_transpose2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"
   },
   {
-    "name": "quantized::embedding_bag_4bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "indices", "type": "Tensor" },
-      { "name": "offsets", "type": "Tensor?", "default": null },
-      { "name": "scale_grad_by_freq", "type": "boolean", "default": false },
-      { "name": "mode", "type": "int64", "default": 0 },
-      { "name": "pruned_weights", "type": "boolean", "default": false },
-      { "name": "per_sample_weights", "type": "Tensor?", "default": null },
-      { "name": "compressed_indices_mapping", "type": "Tensor?", "default": null },
-      { "name": "include_last_offset", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::conv_transpose2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"
   },
   {
-    "name": "quantized::embedding_bag_byte_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor",
-    "inputs": [
-      { "name": "weight", "type": "Tensor" },
-      { "name": "indices", "type": "Tensor" },
-      { "name": "offsets", "type": "Tensor?", "default": null },
-      { "name": "scale_grad_by_freq", "type": "boolean", "default": false },
-      { "name": "mode", "type": "int64", "default": 0 },
-      { "name": "pruned_weights", "type": "boolean", "default": false },
-      { "name": "per_sample_weights", "type": "Tensor?", "default": null },
-      { "name": "compressed_indices_mapping", "type": "Tensor?", "default": null },
-      { "name": "include_last_offset", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::embedding_bag_4bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"
   },
   {
-    "name": "quantized::embedding_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor",
-    "inputs": [
-      { "name": "weight", "type": "__torch__.torch.classes.quantized.EmbeddingPackedParamsBase" },
-      { "name": "indices", "type": "Tensor" },
-      { "name": "pruned_weights", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::embedding_bag_byte_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"
+  },
+  {
+    "name": "quantized::embedding_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor"
   },
   {
     "name": "quantized::hardswish(Tensor input, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Activation",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Activation"
   },
   {
-    "name": "quantized::instance_norm(Tensor input, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::instance_norm(Tensor input, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"
   },
   {
     "name": "quantized::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Normalization",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "normalized_shape", "type": "int64[]" },
-      { "name": "weight", "type": "Tensor?" },
-      { "name": "bias", "type": "Tensor?" },
-      { "name": "eps", "type": "float32" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Normalization"
   },
   {
     "name": "quantized::leaky_relu(Tensor qx, Scalar negative_slope, bool inplace, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Activation",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "negative_slope", "type": "Scalar", "default": false },
-      { "name": "inplace", "type": "boolean" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Activation"
   },
   {
     "name": "quantized::linear(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y",
-    "category": "Layer",
-    "inputs": [
-      { "name": "X", "type": "Tensor" },
-      { "name": "W_prepack", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "Y_scale_i", "type": "float32" },
-      { "name": "Y_zero_point_i", "type": "int64" }
-    ],
-    "outputs": [
-      { "name": "Y", "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "quantized::linear_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y",
-    "category": "Layer",
-    "inputs": [
-      { "name": "X", "type": "Tensor" },
-      { "name": "W_prepack", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "reduce_range", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "name": "Y", "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
-    "name": "quantized::linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack",
-    "inputs": [
-      { "name": "W", "type": "Tensor" },
-      { "name": "B", "type": "Tensor?", "default": null }
-    ],
-    "outputs": [
-      { "name": "W_prepack", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" }
-    ]
+    "name": "quantized::linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"
   },
   {
-    "name": "quantized::linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack",
-    "inputs": [
-      { "name": "W", "type": "Tensor" },
-      { "name": "B", "type": "Tensor?", "default": null }
-    ],
-    "outputs": [
-      { "name": "W_prepack", "type": "Tensor" }
-    ]
+    "name": "quantized::linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"
   },
   {
     "name": "quantized::linear_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y",
-    "category": "Layer",
-    "inputs": [
-      { "name": "X", "type": "Tensor" },
-      { "name": "W_prepack", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "Y_scale_i", "type": "float32" },
-      { "name": "Y_zero_point_i", "type": "int64" }
-    ],
-    "outputs": [
-      { "name": "Y", "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
     "name": "quantized::linear_relu_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y",
-    "category": "Layer",
-    "inputs": [
-      { "name": "X", "type": "Tensor" },
-      { "name": "W_prepack", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "reduce_range", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "name": "Y", "type": "Tensor" }
-    ]
+    "category": "Layer"
   },
   {
-    "name": "quantized::make_quantized_cell_params(Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh) -> __torch__.torch.classes.rnn.CellParamsBase",
-    "inputs": [
-      { "name": "w_ih", "type": "Tensor" },
-      { "name": "w_hh", "type": "Tensor" },
-      { "name": "b_ih", "type": "Tensor" },
-      { "name": "b_hh", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.rnn.CellParamsBase" }
-    ]
+    "name": "quantized::make_quantized_cell_params(Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh) -> __torch__.torch.classes.rnn.CellParamsBase"
   },
   {
-    "name": "quantized::make_quantized_cell_params_dynamic(__torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor bias_ih, Tensor bias_hh, bool reduce_range=False) -> __torch__.torch.classes.rnn.CellParamsBase",
-    "inputs": [
-      { "name": "w_ih", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "w_hh", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "bias_ih", "type": "Tensor" },
-      { "name": "bias_hh", "type": "Tensor" },
-      { "name": "reduce_range", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.rnn.CellParamsBase" }
-    ]
+    "name": "quantized::make_quantized_cell_params_dynamic(__torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor bias_ih, Tensor bias_hh, bool reduce_range=False) -> __torch__.torch.classes.rnn.CellParamsBase"
   },
   {
-    "name": "quantized::make_quantized_cell_params_fp16(__torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh) -> __torch__.torch.classes.rnn.CellParamsBase",
-    "inputs": [
-      { "name": "w_ih", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "w_hh", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" }
-    ],
-    "outputs": [
-      { "type": "__torch__.torch.classes.rnn.CellParamsBase" }
-    ]
+    "name": "quantized::make_quantized_cell_params_fp16(__torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh) -> __torch__.torch.classes.rnn.CellParamsBase"
   },
   {
-    "name": "quantized::mul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "scale", "type": "float32" },
-      { "name": "zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"
   },
   {
-    "name": "quantized::mul.Scalar(Tensor qa, Scalar b)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul.Scalar(Tensor qa, Scalar b) -> Tensor qc"
   },
   {
-    "name": "quantized::mul.Scalar2(Scalar b, Tensor qa)-> Tensor qc",
-    "inputs": [
-      { "name": "b", "type": "Scalar" },
-      { "name": "qa", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul.Scalar2(Scalar b, Tensor qa) -> Tensor qc"
   },
   {
-    "name": "quantized::mul.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_relu(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "scale", "type": "float32" },
-      { "name": "zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_relu(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"
   },
   {
-    "name": "quantized::mul_relu.Scalar(Tensor qa, Scalar b)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_relu.Scalar(Tensor qa, Scalar b) -> Tensor qc"
   },
   {
-    "name": "quantized::mul_relu.Scalar2(Scalar b, Tensor qa)-> Tensor qc",
-    "inputs": [
-      { "name": "b", "type": "Scalar" },
-      { "name": "qa", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_relu.Scalar2(Scalar b, Tensor qa) -> Tensor qc"
   },
   {
-    "name": "quantized::mul_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_relu.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_relu.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_relu_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "qb", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_relu_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_scalar(Tensor qa, Scalar b)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar(Tensor qa, Scalar b) -> Tensor qc"
   },
   {
-    "name": "quantized::mul_scalar.Tensor(Tensor qa, Tensor b)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar.Tensor(Tensor qa, Tensor b) -> Tensor qc"
   },
   {
-    "name": "quantized::mul_scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_scalar_relu(Tensor qa, Scalar b)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar_relu(Tensor qa, Scalar b) -> Tensor qc"
   },
   {
-    "name": "quantized::mul_scalar_relu.Tensor(Tensor qa, Tensor b)-> Tensor qc",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "qc", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar_relu.Tensor(Tensor qa, Tensor b) -> Tensor qc"
   },
   {
-    "name": "quantized::mul_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Scalar" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::mul_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out",
-    "inputs": [
-      { "name": "qa", "type": "Tensor" },
-      { "name": "b", "type": "Tensor" },
-      { "name": "out", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "name": "out", "type": "Tensor" }
-    ]
+    "name": "quantized::mul_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"
   },
   {
-    "name": "quantized::prelu(Tensor qx, Tensor weight, float output_scale, int output_zero_point) -> Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "weight", "type": "Tensor" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::prelu(Tensor qx, Tensor weight, float output_scale, int output_zero_point) -> Tensor"
   },
   {
-    "name": "quantized::quantized_gru_cell_dynamic(Tensor input, Tensor hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor b_ih, Tensor b_hh) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "hx", "type": "Tensor" },
-      { "name": "w_ih", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "w_hh", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "b_ih", "type": "Tensor" },
-      { "name": "b_hh", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::quantized_gru_cell_dynamic(Tensor input, Tensor hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor b_ih, Tensor b_hh) -> Tensor"
   },
   {
-    "name": "quantized::quantized_lstm_cell_dynamic(Tensor input, Tensor[] hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor bias_ih, Tensor bias_hh) -> (Tensor, Tensor)",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "hx", "type": "Tensor[]" },
-      { "name": "w_ih", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "w_hh", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "bias_ih", "type": "Tensor" },
-      { "name": "bias_hh", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::quantized_lstm_cell_dynamic(Tensor input, Tensor[] hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor bias_ih, Tensor bias_hh) -> (Tensor, Tensor)"
   },
   {
-    "name": "quantized::quantized_rnn_relu_cell_dynamic(Tensor input, Tensor hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor b_ih, Tensor b_hh) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "hx", "type": "Tensor" },
-      { "name": "w_ih", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "w_hh", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "b_ih", "type": "Tensor" },
-      { "name": "b_hh", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::quantized_rnn_relu_cell_dynamic(Tensor input, Tensor hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor b_ih, Tensor b_hh) -> Tensor"
   },
   {
-    "name": "quantized::quantized_rnn_tanh_cell_dynamic(Tensor input, Tensor hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor b_ih, Tensor b_hh) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "hx", "type": "Tensor" },
-      { "name": "w_ih", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "w_hh", "type": "__torch__.torch.classes.quantized.LinearPackedParamsBase" },
-      { "name": "b_ih", "type": "Tensor" },
-      { "name": "b_hh", "type": "Tensor" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::quantized_rnn_tanh_cell_dynamic(Tensor input, Tensor hx, __torch__.torch.classes.quantized.LinearPackedParamsBase w_ih, __torch__.torch.classes.quantized.LinearPackedParamsBase w_hh, Tensor b_ih, Tensor b_hh) -> Tensor"
   },
   {
     "name": "quantized::relu6(Tensor qx, bool inplace=False) -> Tensor",
-    "category": "Activation",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "inplace", "type": "boolean", "default": false }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Activation"
   },
   {
     "name": "quantized::sigmoid(Tensor qx, float output_scale, int output_zero_point) -> Tensor",
-    "category": "Activation",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "category": "Activation"
   },
   {
-    "name": "quantized::softmax(Tensor qx, int dim, float output_scale, int output_zero_point) -> Tensor",
-    "inputs": [
-      { "name": "qx", "type": "Tensor" },
-      { "name": "dim", "type": "int64" },
-      { "name": "output_scale", "type": "float32" },
-      { "name": "output_zero_point", "type": "int64" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "quantized::softmax(Tensor qx, int dim, float output_scale, int output_zero_point) -> Tensor"
   },
   {
     "name": "torch.nn.modules.activation.ELU",
@@ -20411,42 +6846,12 @@
     "category": "Data"
   },
   {
-    "name": "torchaudio::sox_effects_apply_effects_tensor(Tensor tensor, int sample_rate, str[][] effects, bool channels_first=True) -> (Tensor, int)",
-    "inputs": [
-      { "name": "tensor", "type": "Tensor" },
-      { "name": "sample_rate", "type": "int64" },
-      { "name": "effects", "type": "string[][]" },
-      { "name": "channels_first", "type": "boolean", "default": true }
-    ],
-    "outputs": [
-      { "type": "Tensor" },
-      { "type": "int64" }
-    ]
+    "name": "torchaudio::sox_effects_apply_effects_tensor(Tensor tensor, int sample_rate, str[][] effects, bool channels_first=True) -> (Tensor, int)"
   },
   {
-    "name": "torchvision::nms(Tensor dets, Tensor scores, float iou_threshold) -> Tensor",
-    "inputs": [
-      { "name": "dets", "type": "Tensor" },
-      { "name": "scores", "type": "Tensor" },
-      { "name": "iou_threshold", "type": "float32" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "torchvision::nms(Tensor dets, Tensor scores, float iou_threshold) -> Tensor"
   },
   {
-    "name": "torchvision::roi_align(Tensor input, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int sampling_ratio, bool aligned) -> Tensor",
-    "inputs": [
-      { "name": "input", "type": "Tensor" },
-      { "name": "rois", "type": "Tensor" },
-      { "name": "spatial_scale", "type": "float32" },
-      { "name": "pooled_height", "type": "int64" },
-      { "name": "pooled_width", "type": "int64" },
-      { "name": "sampling_ratio", "type": "int64" },
-      { "name": "aligned", "type": "boolean" }
-    ],
-    "outputs": [
-      { "type": "Tensor" }
-    ]
+    "name": "torchvision::roi_align(Tensor input, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int sampling_ratio, bool aligned) -> Tensor"
   }
 ]
\ No newline at end of file
diff --git a/source/pytorch.js b/source/pytorch.js
index 28baf928a5..c1175c0a38 100644
--- a/source/pytorch.js
+++ b/source/pytorch.js
@@ -156,7 +156,7 @@ pytorch.Graph = class {
                 if (node.kind() === 'prim::ListConstruct') {
                     if (node.outputs().length === 1 &&
                         node.outputs().every((output) => output.uses().length === 1) &&
-                        node.inputs().every((input) => pytorch.Utility.isTensor(input.value))) {
+                        node.inputs().every((input) => pytorch.Utility.isTensor(input.value) || pytorch.Utility.isInstance(input, 'torch.Value'))) {
                         continue;
                     }
                     if (node.inputs().length === 0 &&
@@ -476,10 +476,11 @@ pytorch.Node = class {
                         if (Array.isArray(input.value) && input.value.every((value) => pytorch.Utility.isTensor(value))) {
                             continue;
                         }
-                        argument = new pytorch.Argument(name, input.value, 'attribute');
+                        const type = input.type() ? pytorch.Utility.toType(input.type()) : null;
+                        argument = new pytorch.Argument(name, input.value, type || 'attribute');
                     } else if (pytorch.Utility.isInstance(input.type(), 'torch.ListType')) {
                         if (input.node() && input.node().kind() === 'prim::ListConstruct' && input.uses().length === 1 &&
-                            input.node().inputs().every((value) => pytorch.Utility.isInstance(value.type(), 'torch.IntType') || pytorch.Utility.isInstance(value.type(), 'torch.FloatType') || pytorch.Utility.isInstance(value.type(), 'torch.StringType') || pytorch.Utility.isInstance(value.type(), 'torch.ComplexType') || pytorch.Utility.isInstance(value.type(), 'torch.TensorType'))) {
+                            input.node().inputs().every((value) => pytorch.Utility.isInstance(value, 'torch.Value') || pytorch.Utility.isInstance(value.type(), 'torch.IntType') || pytorch.Utility.isInstance(value.type(), 'torch.FloatType') || pytorch.Utility.isInstance(value.type(), 'torch.StringType') || pytorch.Utility.isInstance(value.type(), 'torch.ComplexType') || pytorch.Utility.isInstance(value.type(), 'torch.TensorType'))) {
                             const list = input.node().inputs();
                             const args = list.map((value) => {
                                 if (value.uses().length === 1 && value.node() === input.node() && value.value !== undefined) {
@@ -505,7 +506,7 @@ pytorch.Node = class {
                     }
                 } else if (pytorch.Utility.isTensor(input.value) || input.value === undefined || input.value === null) {
                     let list = [input];
-                    if (input.node() &&
+                    if (input.node() && node !== input.node() &&
                         input.node().kind() === 'prim::ListConstruct' &&
                         input.uses().length === 1 &&
                         input.node().inputs().every((input) => pytorch.Utility.isTensor(input.value))) {
@@ -1732,6 +1733,8 @@ pytorch.Execution = class extends python.Execution {
                     } else if (pytorch.Utility.isTensor(result)) {
                         const value = execution.variable(result);
                         execution.graph.return_node().addInput(value);
+                    } else if (result instanceof torch.Value) {
+                        execution.graph.return_node().addInput(result);
                     } else if (Object(result) === result) {
                         for (const key of Object.keys(result)) {
                             const item = result[key];
@@ -2184,18 +2187,18 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                 return this._blocks;
             }
             addInput(value) {
-                const use = execution.invoke('torch.Use', [this]);
+                const use = new torch.Use(this);
                 value.uses().push(use);
                 this._inputs.push(value);
                 return value;
             }
             addOutput() {
-                const value = execution.invoke('torch.Value', [this]);
+                const value = new torch.Value(this);
                 this._outputs.push(value);
                 return value;
             }
             addBlock() {
-                const block = execution.invoke('torch.Block', [this._graph, this]);
+                const block = new torch.Block(this._graph, this);
                 this._blocks.push(block);
                 return block;
             }
@@ -2669,7 +2672,7 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                             node.addInput(value);
                             output.setType(torch.ListType.get(torch.StringType.get()));
                         } else if (pytorch.Utility.isTensor(item)) {
-                            const value = this.variable(item, node);
+                            const value = this.variable(item, null);
                             node.addInput(value);
                             output.setType(torch.ListType.get(torch.TensorType.get()));
                         } else {
@@ -2738,12 +2741,13 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                         throw new python.Error(`Unsupported dict item type '${pair.type}'.`);
                     }
                     const key = this.expression(pair.key, context);
-                    const keyValue = this.variable(key, node);
+                    const keyValue = this.variable(key, null);
                     keyType = keyValue.type();
                     const value = this.expression(pair.value, context);
-                    const valueValue = this.variable(value, node);
+                    const valueValue = this.variable(value, null);
                     valueType = valueValue.type();
-                    this.variable(value, node);
+                    node.addInput(keyValue);
+                    node.addInput(valueValue);
                 }
                 const output = node.addOutput();
                 if (keyType && valueType) {
@@ -2896,78 +2900,61 @@ pytorch.jit.Execution = class extends pytorch.Execution {
             return super.call(target, name, args, context);
         }
         const [schema, evalArgs] = overload;
-        const copyArgs = Array.prototype.slice.call(args);
-        const copyEvalArgs = Array.prototype.slice.call(evalArgs);
-        const index = schema.name.indexOf('(');
-        const op = index === -1 ? schema.name : schema.name.substring(0, index);
+        const op = schema.overload_name ? `${schema.name}.${schema.overload_name}` : schema.name;
         const node = this._graph.create(op);
         const referencedParameters = [];
-        const parameters = Array.prototype.slice.call(schema.inputs || []).concat(Array.prototype.slice.call(schema.attributes || []));
-        const types = new Map();
-        while (copyEvalArgs.length > 0) {
-            if (parameters.length <= 0) {
-                if (schema.is_vararg || schema.name.startsWith('_caffe2::')) {
+        const parameters = schema.arguments;
+        const varTypes = new Map();
+        varTypes.map = function(type) {
+            if (type.kind() === 'VarType') {
+                const key = type.annotation_str();
+                if (!varTypes.has(key)) {
+                    throw new pytorch.Error(`Unknown var type '${key}'.`);
+                }
+                return varTypes.get(key);
+            }
+            return type;
+        };
+        let position = 0;
+        let index = 0;
+        while (position < evalArgs.length) {
+            if (index >= parameters.length) {
+                if (schema.name.startsWith('_caffe2::') || schema.is_vararg) {
                     break;
                 }
                 throw new pytorch.Error('Invalid parameter length.');
             }
-            if (copyArgs.every((arg) => arg.type === '=' && arg.target && arg.target.type === 'id') &&
-                parameters.every((parameter) => parameter.type !== 'Tensor' && parameter.type !== 'Tensor[]')) {
-                const map = new Map(parameters.map((parameter) => [parameter.name, parameter]));
-                while (copyArgs.length > 0) {
-                    const argument = copyArgs.shift();
-                    const arg = copyEvalArgs.shift();
-                    const parameter = map.get(argument.target.value);
-                    if (!parameter) {
-                        throw new pytorch.Error();
-                    }
-                    let type = parameter.type;
-                    let optional = false;
-                    if (type.endsWith('?')) {
-                        type = parameter.type.substring(0, parameter.type.length - 1);
-                        optional = true;
-                    }
-                    if (!this.isType(arg, type)) {
-                        if (optional) {
-                            continue;
-                        }
-                        throw new pytorch.Error();
-                    }
-                    const value = this.variable(arg);
-                    value.value = arg;
-                    node.addInput(value);
-                }
-                continue;
+            const arg = parameters[index];
+            if (arg.kwarg_only) {
+                break;
             }
-            const parameter = parameters.shift();
-            const [argument] = copyEvalArgs;
+            index++;
+            const v = evalArgs[position];
             let match = false;
             let input = null;
             let optional = false;
-            let type = parameter.type;
-            if (type.endsWith('?')) {
-                type = parameter.type.slice(0, -1);
+            let type = arg.real_type;
+            if (type instanceof torch.OptionalType) {
+                type = type.getElementType();
                 optional = true;
             }
             if (optional === true &&
-                (type === 'float32' || type === 'boolean' || type === 'int64' || type === 'complex') &&
-                argument instanceof torch.Value && argument.type() instanceof torch.NoneType) {
-                copyArgs.shift();
-                copyEvalArgs.shift();
-                input = argument;
+                (type instanceof torch.FloatType || type instanceof torch.BoolType || type instanceof torch.IntType || type instanceof torch.ComplexType || type.kind() === 'ScalarTypeType' || type instanceof torch.DeviceObjType || type.kind() === 'LayoutKind') &&
+                v instanceof torch.Value && v.type() instanceof torch.NoneType) {
+                position++;
+                input = v;
                 match = true;
-            } else if (type === 'Tensor[]') {
-                const [argument] = copyEvalArgs;
-                if ((argument instanceof torch.Value && pytorch.Utility.toType(argument.type()) === 'Tensor[]') ||
-                    (Array.isArray(argument) && argument.every((item) => pytorch.Utility.isTensor(item) || item === null || (item instanceof torch.Value && item.type() instanceof torch.TensorType)))) {
-                    copyArgs.shift();
-                    copyEvalArgs.shift();
-                    if (argument instanceof torch.Value) {
-                        input = argument;
+            } else if (type instanceof torch.ListType && type.getElementType() instanceof torch.TensorType) {
+                const v = evalArgs[position];
+                if ((v instanceof torch.Value && v.type() instanceof torch.ListType && v.type().getElementType() instanceof torch.TensorType) ||
+                    (Array.isArray(v) && v.every((item) => pytorch.Utility.isTensor(item) || item === null || (item instanceof torch.Value && item.type() instanceof torch.TensorType)))) {
+                    position++;
+                    if (v instanceof torch.Value) {
+                        input = v;
                         match = true;
                     } else {
                         const list = this._graph.create('prim::ListConstruct');
-                        for (const arg of argument) {
+                        for (const arg of v) {
                             const tensor = arg;
                             if (tensor) {
                                 tensor.__count__ = (tensor.__count__ || 0) + 1;
@@ -2987,281 +2974,108 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     }
                     throw new pytorch.Error();
                 }
+            } else if (!this.isNativeType(v, type, arg.N) && v !== null) {
+                if (optional) {
+                    continue;
+                }
+                throw new pytorch.Error('Invalid argument type.');
+            } else if (args[position].type === '=' && args[position].target.value !== arg.name) {
+                throw new pytorch.Error('Expected named argument.');
             } else {
-                const [arg] = copyArgs;
-                if (!this.isType(argument, type) && argument !== null) {
-                    if (optional) {
-                        continue;
-                    }
-                    throw new pytorch.Error('Invalid argument type.');
-                } else if (arg.type === '=') {
-                    throw new pytorch.Error('Expected named argument.');
+                position++;
+                if (v instanceof torch.Value) {
+                    input = v;
+                    match = true;
                 } else {
-                    copyArgs.shift();
-                    copyEvalArgs.shift();
-                    if (pytorch.Utility.isInstance(argument, 'torch.Value')) {
-                        input = argument;
-                        match = true;
-                    } else {
-                        const value = this.variable(argument);
-                        value.value = argument;
-                        input = value;
-                        match = true;
-                    }
+                    const value = this.variable(v);
+                    value.value = v;
+                    input = value;
+                    match = true;
                 }
             }
             if (match) {
                 node.addInput(input);
-                if (type === 't') {
-                    const key = type;
+                if (type.kind() === 'VarType') {
+                    const key = type.annotation_str();
                     if (input instanceof torch.Value && input.type()) {
-                        types.set(key, input.type());
+                        varTypes.set(key, input.type());
                     } else if (input instanceof torch.Value && Number.isInteger(input.value)) {
-                        types.set(key, torch.IntType.get());
+                        varTypes.set(key, torch.IntType.get());
                     }
                     // throw new pytorch.Error("Unknown value type 't'.");
                 }
-                if (type === 't[]') {
-                    const key = type.replace('[]', '');
+                if (type instanceof torch.ListType && type.getElementType().kind() === 'VarType') {
+                    const key = type.getElementType().annotation_str();
                     if (input instanceof torch.Value && input.type() instanceof torch.OptionalType && input.type().getElementType() instanceof torch.ListType) {
-                        types.set(key, input.type().getElementType().getElementType());
+                        varTypes.set(key, input.type().getElementType().getElementType());
                     } else if (input instanceof torch.Value && input.type() instanceof torch.ListType) {
-                        types.set(key, input.type().getElementType());
+                        varTypes.set(key, input.type().getElementType());
                     } else if (Array.isArray(input) && input.length > 0 && input.every((item) => Number.isInteger(item))) {
-                        types.set(key, torch.IntType.get());
+                        varTypes.set(key, torch.IntType.get());
                     } else if (input.value && Array.isArray(input.value) && input.value.length > 0 && input.value.every((item) => Number.isInteger(item) || isNaN(item))) {
-                        types.set(key, torch.IntType.get());
+                        varTypes.set(key, torch.IntType.get());
                     } else if (input.value && Array.isArray(input.value) && input.value.length > 0 && input.value.every((item) => pytorch.Utility.isTensor(item))) {
-                        types.set(key, torch.TensorType.get());
+                        varTypes.set(key, torch.TensorType.get());
                     } else {
                         // throw new pytorch.Error("Unknown value type 't'.");
                         continue;
                     }
                 }
-                const dict = type.match(/^Dict\((\w+),\s*(\w+)\)$/);
-                if (dict && (dict[2] === 't' || dict[2] === 'tVal')) {
+                if (type instanceof torch.DictType && type.getValueType().kind() === 'VarType') {
+                    const key = type.getValueType().annotation_str();
                     if (input instanceof torch.Value && input.type() instanceof torch.DictType) {
-                        types.set(dict[2], input.type().getValueType());
+                        varTypes.set(key, input.type().getValueType());
                     } else if (input.value && Object.values(input.value).every((item) => pytorch.Utility.isTensor(item))) {
-                        types.set(dict[2], input.type().getValueType());
+                        varTypes.set(key, input.type().getValueType());
                     } else {
                         throw new pytorch.Error("Unknown dict type 't[]'.");
                     }
                 }
-                const tuple = type.match(/^\((\w+),\s*(\w+)\)\[\]$/);
-                if (tuple && (tuple[2] === 'tVal')) {
+                if (type instanceof torch.ListType && type.getElementType() instanceof torch.TupleType && type.getElementType().elements().length === 2 && type.getElementType().elements()[1].kind() === 'VarType') {
+                    const key = type.getElementType().elements()[1].annotation_str();
                     if (input instanceof torch.Value && input.type() instanceof torch.ListType && input.type().getElementType() instanceof torch.TupleType) {
                         const elements = input.type().getElementType().elements();
                         if (elements.length === 2) {
-                            types.set(tuple[2], elements[1]);
+                            varTypes.set(key, elements[1]);
                         }
                     }
                 }
             }
         }
+        if (args.every((arg, index) => index < position || (arg.type === '=' && arg.target && arg.target.type === 'id'))) {
+            const params = new Map(parameters.slice(index).map((a) => [a.name, a]));
+            while (position < args.length) {
+                const v = evalArgs[position];
+                const arg = params.get(args[position].target.value);
+                position++;
+                if (!arg) {
+                    throw new pytorch.Error();
+                }
+                let type = arg.real_type;
+                let optional = false;
+                if (type instanceof torch.OptionalType) {
+                    type = type.getElementType();
+                    optional = true;
+                }
+                if (!this.isNativeType(v, type)) {
+                    if (optional) {
+                        continue;
+                    }
+                    throw new pytorch.Error();
+                }
+                const value = this.variable(v);
+                value.value = v;
+                node.addInput(value);
+            }
+        }
         const result = [];
-        for (let i = 0; i < schema.outputs.length; i++) {
-            const parameter = schema.outputs[i];
-            const type = parameter.type;
-            switch (type) {
+        for (let i = 0; i < schema.returns.length; i++) {
+            const arg = schema.returns[i];
+            const type = arg.real_type;
+            switch (type.str()) {
                 case 'Tensor': {
-                    const output = this.invoke('torch.Tensor', []);
+                    const output = this.createTensorOutput(schema.name, evalArgs, i);
                     output.__origin__ = schema.name;
-                    if (i === 0) {
-                        switch (schema.name) {
-                            case 'aten::conv1d':
-                            case 'aten::embedding': {
-                                output.resize_([NaN, NaN, NaN]);
-                                break;
-                            }
-                            case 'aten::cat':
-                            case 'aten::conv2d':
-                            case 'aten::dropout':
-                            case 'aten::flatten':
-                            case 'aten::flatten.named_out_dim':
-                            case 'aten::max_pool2d':
-                            case 'aten::adaptive_avg_pool2d':
-                            case 'aten::avg_pool2d':
-                            case 'aten::quantize_per_tensor':
-                            case 'aten::relu_':
-                            case 'aten::prelu':
-                            case 'aten::hardtanh_':
-                            case 'aten::upsample_bilinear2d':
-                            case 'prepacked::conv2d_clamp_run': {
-                                const [input] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && input.size() === undefined) {
-                                    input.resize_([NaN, NaN, NaN, NaN]);
-                                }
-                                output.resize_([NaN, NaN, NaN, NaN]);
-                                break;
-                            }
-                            case 'aten::slice':
-                            case 'aten::slice.Tensor': {
-                                const [input] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
-                                    const size = input.size();
-                                    output.resize_(size);
-                                }
-                                break;
-                            }
-                            case 'aten::to':
-                            case 'aten::to.device':
-                            case 'aten::to.dtype':
-                            case 'aten::to.dtype_layout': {
-                                const [input] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
-                                    const size = input.size();
-                                    output.resize_(size);
-                                }
-                                break;
-                            }
-                            case 'aten::conv3d': {
-                                output.resize_([NaN, NaN, NaN, NaN, NaN]);
-                                break;
-                            }
-                            case 'aten::roll':
-                            case 'aten::detach':
-                            case 'aten::mean':
-                            case 'aten::mul':
-                            case 'aten::mul.Scalar':
-                            case 'aten::div':
-                            case 'aten::div.Scalar':
-                            case 'aten::batch_norm':
-                            case 'aten::gelu':
-                            case 'aten::relu':
-                            case 'aten::clamp':
-                            case 'aten::clamp_':
-                            case 'aten::_add_relu_':
-                            case 'aten::hardswish_': {
-                                const [input] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
-                                    output.resize_(input.size());
-                                }
-                                break;
-                            }
-                            case 'aten::add':
-                            case 'aten::add.Scalar':
-                            case 'aten::sub':
-                            case 'aten::sub.Scalar': {
-                                const [input] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
-                                    output.resize_(input.size());
-                                } else {
-                                    const [, other] = evalArgs;
-                                    if (pytorch.Utility.isTensor(other) && Array.isArray(other.size())) {
-                                        output.resize_(other.size());
-                                    }
-                                }
-                                break;
-                            }
-                            case 'aten::select':
-                            case 'aten::select.int': {
-                                const [input] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
-                                    output.resize_(Array(input.size().length - 1).fill(NaN));
-                                }
-                                break;
-                            }
-                            case 'aten::layer_norm': {
-                                const [input, normalized_shape] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
-                                    const shape = input.size();
-                                    if (Array.isArray(normalized_shape) && normalized_shape.length === 1) {
-                                        const [value] = normalized_shape;
-                                        shape[shape.length - 1] = value;
-                                    }
-                                    output.resize_(shape);
-                                }
-                                break;
-                            }
-                            case 'aten::empty':
-                            case 'aten::ones':
-                            case 'aten::zeros':
-                            case 'aten::zeros_like': {
-                                output.resize_(evalArgs[0]);
-                                break;
-                            }
-                            case 'aten::view':
-                            case 'aten::reshape':
-                            case 'aten::new_full': {
-                                output.resize_(evalArgs[1]);
-                                break;
-                            }
-                            case 'aten::squeeze':
-                            case 'aten::squeeze.dim': {
-                                const [input] = evalArgs;
-                                if (input instanceof torch.Value === false) {
-                                    const size = input.size();
-                                    if (Array.isArray(size)) {
-                                        switch (evalArgs.length) {
-                                            case 1: {
-                                                output.resize_(size.filter((value) => value !== 1));
-                                                break;
-                                            }
-                                            case 2: {
-                                                const [, dim] = evalArgs;
-                                                output.resize_(size.filter((value, index) => (value !== 1 && !isNaN(value)) || index !== dim));
-                                                break;
-                                            }
-                                            default: {
-                                                break;
-                                            }
-                                        }
-                                    }
-                                }
-                                break;
-                            }
-                            case 'aten::unsqueeze': {
-                                const [input, dim] = evalArgs;
-                                if (pytorch.Utility.isTensor(input)) {
-                                    const size = input.size();
-                                    if (Array.isArray(size) && dim !== undefined) {
-                                        const shape = size.slice();
-                                        shape.splice(dim, 0, 1);
-                                        output.resize_(shape);
-                                    } else {
-                                        output.resize_([NaN, NaN, NaN, NaN]);
-                                    }
-                                }
-                                break;
-                            }
-                            case 'aten::transpose':
-                            case 'aten::transpose.int': {
-                                const [input, dim0, dim1] = evalArgs;
-                                if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
-                                    const size = input.size().slice();
-                                    const d0 = dim0 >= 0 ? dim0 : size.length + dim0;
-                                    const d1 = dim1 >= 0 ? dim1 : size.length + dim1;
-                                    const value = size[dim0];
-                                    /* eslint-disable prefer-destructuring */
-                                    size[d0] = size[1];
-                                    /* eslint-enable prefer-destructuring */
-                                    size[d1] = value;
-                                    output.resize_(size);
-                                }
-                                break;
-                            }
-                            case 'aten::contiguous': {
-                                const [source] = evalArgs;
-                                output.__source__ = source;
-                                break;
-                            }
-                            case 'quantized::cat':
-                            case 'quantized::cat_relu':
-                            case 'quantized::linear':
-                            case 'quantized::conv2d':
-                            case 'quantized::conv2d.new':
-                            case 'quantized::conv2d_relu':
-                            case 'quantized::conv2d_relu.new':
-                            case 'quantized::add':
-                            case 'quantized::add_relu':
-                                output.resize_([NaN, NaN, NaN, NaN]);
-                                output.__quantized__ = true;
-                                break;
-                            default:
-                                break;
-                        }
-                    }
                     this.variable(output, node);
                     result.push(output);
                     break;
@@ -3294,25 +3108,9 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                         default:
                             break;
                     }
-
                     const value = node.addOutput();
                     value.setType(torch.ListType.get(torch.TensorType.get()));
                     result.push(value);
-
-                    /*
-                    const value = node.addOutput();
-                    const list = this._graph.create('prim::ListUnpack');
-                    list.addInput(value);
-
-                    const tensors = [];
-                    for (let i = 0; i < count; i ++) {
-                        const tensor = this.invoke('torch.Tensor', []);
-                        tensor.__origin__ = schema.name;
-                        this.variable(tensor, list);
-                        tensors.push(tensor);
-                    }
-                    result.push(tensors);
-                    */
                     break;
                 }
                 case '__torch__.torch.classes.quantized.Conv2dPackedParamsBase':
@@ -3322,40 +3120,12 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                 case '__torch__.torch.classes.xnnpack.Conv2dOpContext':
                 case '__torch__.torch.classes.xnnpack.LinearOpContext':
                 case '__torch__.torch.classes.xnnpack.TransposeConv2dOpContext': {
-                    const value = this.invoke(parameter.type, []);
+                    const value = this.invoke(type.qualified_name(), []);
                     this.variable(value, node);
                     result.push(value);
                     break;
                 }
-                case 'Scalar': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.NumberType.get());
-                    result.push(value);
-                    break;
-                }
-                case 'boolean': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.BoolType.get());
-                    result.push(value);
-                    break;
-                }
-                case 'boolean[]': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.ListType.get(torch.BoolType.get()));
-                    result.push(value);
-                    break;
-                }
-                case 'string[]': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.ListType.get(torch.StringType.get()));
-                    result.push(value);
-                    break;
-                }
-                case 'int64': {
+                case 'int': {
                     const value = this.variable(null, node);
                     value.__origin__ = schema.name;
                     value.setType(torch.IntType.get());
@@ -3369,7 +3139,7 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     result.push(value);
                     break;
                 }
-                case 'int64[]': {
+                case 'int[]': {
                     const value = this.variable(null, node);
                     value.__origin__ = schema.name;
                     value.setType(torch.ListType.get(torch.IntType.get()));
@@ -3380,31 +3150,18 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     result.push(value);
                     break;
                 }
-                case 'float32': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.FloatType.get());
-                    result.push(value);
-                    break;
-                }
-                case 'float32[]': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.ListType.get(torch.FloatType.get()));
-                    result.push(value);
-                    break;
-                }
-                case 'complex': {
+                case 'Scalar':
+                case 'Dict(str, Tensor)':
+                case 'str':
+                case 'str[]':
+                case 'float':
+                case 'float[]':
+                case 'complex':
+                case 'bool':
+                case 'bool[]': {
                     const value = this.variable(null, node);
                     value.__origin__ = schema.name;
-                    value.setType(torch.ComplexType.get());
-                    result.push(value);
-                    break;
-                }
-                case 'string': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.StringType.get());
+                    value.setType(type);
                     result.push(value);
                     break;
                 }
@@ -3415,17 +3172,10 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     result.push(value);
                     break;
                 }
-                case 'Dict(string, Tensor)': {
-                    const value = this.variable(null, node);
-                    value.__origin__ = schema.name;
-                    value.setType(torch.DictType.get(torch.StringType.get(), torch.TensorType.get()));
-                    result.push(value);
-                    break;
-                }
                 case 't': {
                     const value = this.variable(null, node);
                     value.__origin__ = schema.name;
-                    const t = types.get('t');
+                    const t = varTypes.map(type);
                     if (!t) {
                         throw new pytorch.Error(`Unknown var type 't'.`);
                     }
@@ -3436,7 +3186,7 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                 case 't[]': {
                     const value = this.variable(null, node);
                     value.__origin__ = schema.name;
-                    const t = types.get('t');
+                    const t = varTypes.map(type.getElementType());
                     if (!t) {
                         throw new pytorch.Error();
                     }
@@ -3445,22 +3195,20 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     break;
                 }
                 default: {
-                    const dict = parameter.type.match(/^Dict\((\w+),\s*(\w+)\)$/);
-                    if (dict) {
+                    if (type instanceof torch.DictType) {
                         const value = this.variable(null, node);
                         value.__origin__ = schema.name;
-                        const keyType = this.toType(dict[1], types);
-                        const valueType = this.toType(dict[2], types);
+                        const keyType = varTypes.map(type.getKeyType());
+                        const valueType = varTypes.map(type.getValueType());
                         value.setType(torch.DictType.get(keyType, valueType));
                         result.push(value);
                         break;
                     }
-                    const tuple = type.match(/^\((\w+),\s*(\w+)\)\[\]$/);
-                    if (tuple && (tuple[2] === 't' || tuple[2] === 'tVal')) {
+                    if (type instanceof torch.TupleType && type.elements().length === 2) {
                         const value = this.variable(null, node);
                         value.__origin__ = schema.name;
-                        const keyType = this.toType(tuple[1], types);
-                        const valueType = this.toType(tuple[2], types);
+                        const keyType = varTypes.map(type.elements()[0]);
+                        const valueType = varTypes.map(type.elements()[1]);
                         value.setType(torch.ListType.get(torch.TupleType.get([keyType, valueType])));
                         result.push(value);
                         break;
@@ -3483,7 +3231,208 @@ pytorch.jit.Execution = class extends pytorch.Execution {
         return result[0];
     }
 
-    isNativeType(obj, type) {
+    createTensorOutput(op_name, evalArgs, i) {
+        const torch = this.torch;
+        const output = new torch.Tensor();
+        if (i === 0) {
+            switch (op_name) {
+                case 'aten::conv1d':
+                case 'aten::embedding': {
+                    output.resize_([NaN, NaN, NaN]);
+                    break;
+                }
+                case 'aten::cat':
+                case 'aten::conv2d':
+                case 'aten::dropout':
+                case 'aten::flatten':
+                case 'aten::flatten.named_out_dim':
+                case 'aten::max_pool2d':
+                case 'aten::adaptive_avg_pool2d':
+                case 'aten::avg_pool2d':
+                case 'aten::quantize_per_tensor':
+                case 'aten::relu_':
+                case 'aten::prelu':
+                case 'aten::hardtanh_':
+                case 'aten::upsample_bilinear2d':
+                case 'prepacked::conv2d_clamp_run': {
+                    const [input] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && input.size() === undefined) {
+                        input.resize_([NaN, NaN, NaN, NaN]);
+                    }
+                    output.resize_([NaN, NaN, NaN, NaN]);
+                    break;
+                }
+                case 'aten::slice':
+                case 'aten::slice.Tensor': {
+                    const [input] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
+                        const size = input.size();
+                        output.resize_(size);
+                    }
+                    break;
+                }
+                case 'aten::to':
+                case 'aten::to.device':
+                case 'aten::to.dtype':
+                case 'aten::to.dtype_layout': {
+                    const [input] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
+                        const size = input.size();
+                        output.resize_(size);
+                    }
+                    break;
+                }
+                case 'aten::conv3d': {
+                    output.resize_([NaN, NaN, NaN, NaN, NaN]);
+                    break;
+                }
+                case 'aten::roll':
+                case 'aten::detach':
+                case 'aten::mean':
+                case 'aten::mul':
+                case 'aten::mul.Scalar':
+                case 'aten::div':
+                case 'aten::div.Scalar':
+                case 'aten::batch_norm':
+                case 'aten::gelu':
+                case 'aten::relu':
+                case 'aten::clamp':
+                case 'aten::clamp_':
+                case 'aten::_add_relu_':
+                case 'aten::hardswish_': {
+                    const [input] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
+                        output.resize_(input.size());
+                    }
+                    break;
+                }
+                case 'aten::add':
+                case 'aten::add.Scalar':
+                case 'aten::sub':
+                case 'aten::sub.Scalar': {
+                    const [input] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
+                        output.resize_(input.size());
+                    } else {
+                        const [, other] = evalArgs;
+                        if (pytorch.Utility.isTensor(other) && Array.isArray(other.size())) {
+                            output.resize_(other.size());
+                        }
+                    }
+                    break;
+                }
+                case 'aten::select':
+                case 'aten::select.int': {
+                    const [input] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
+                        output.resize_(Array(input.size().length - 1).fill(NaN));
+                    }
+                    break;
+                }
+                case 'aten::layer_norm': {
+                    const [input, normalized_shape] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
+                        const shape = input.size();
+                        if (Array.isArray(normalized_shape) && normalized_shape.length === 1) {
+                            const [value] = normalized_shape;
+                            shape[shape.length - 1] = value;
+                        }
+                        output.resize_(shape);
+                    }
+                    break;
+                }
+                case 'aten::empty':
+                case 'aten::ones':
+                case 'aten::zeros':
+                case 'aten::zeros_like': {
+                    output.resize_(evalArgs[0]);
+                    break;
+                }
+                case 'aten::view':
+                case 'aten::reshape':
+                case 'aten::new_full': {
+                    output.resize_(evalArgs[1]);
+                    break;
+                }
+                case 'aten::squeeze':
+                case 'aten::squeeze.dim': {
+                    const [input] = evalArgs;
+                    if (input instanceof torch.Value === false) {
+                        const size = input.size();
+                        if (Array.isArray(size)) {
+                            switch (evalArgs.length) {
+                                case 1: {
+                                    output.resize_(size.filter((value) => value !== 1));
+                                    break;
+                                }
+                                case 2: {
+                                    const [, dim] = evalArgs;
+                                    output.resize_(size.filter((value, index) => (value !== 1 && !isNaN(value)) || index !== dim));
+                                    break;
+                                }
+                                default: {
+                                    break;
+                                }
+                            }
+                        }
+                    }
+                    break;
+                }
+                case 'aten::unsqueeze': {
+                    const [input, dim] = evalArgs;
+                    if (pytorch.Utility.isTensor(input)) {
+                        const size = input.size();
+                        if (Array.isArray(size) && dim !== undefined) {
+                            const shape = size.slice();
+                            shape.splice(dim, 0, 1);
+                            output.resize_(shape);
+                        } else {
+                            output.resize_([NaN, NaN, NaN, NaN]);
+                        }
+                    }
+                    break;
+                }
+                case 'aten::transpose':
+                case 'aten::transpose.int': {
+                    const [input, dim0, dim1] = evalArgs;
+                    if (pytorch.Utility.isTensor(input) && Array.isArray(input.size())) {
+                        const size = input.size().slice();
+                        const d0 = dim0 >= 0 ? dim0 : size.length + dim0;
+                        const d1 = dim1 >= 0 ? dim1 : size.length + dim1;
+                        const value = size[dim0];
+                        /* eslint-disable prefer-destructuring */
+                        size[d0] = size[1];
+                        /* eslint-enable prefer-destructuring */
+                        size[d1] = value;
+                        output.resize_(size);
+                    }
+                    break;
+                }
+                case 'aten::contiguous': {
+                    const [source] = evalArgs;
+                    output.__source__ = source;
+                    break;
+                }
+                case 'quantized::cat':
+                case 'quantized::cat_relu':
+                case 'quantized::linear':
+                case 'quantized::conv2d':
+                case 'quantized::conv2d.new':
+                case 'quantized::conv2d_relu':
+                case 'quantized::conv2d_relu.new':
+                case 'quantized::add':
+                case 'quantized::add_relu':
+                    output.resize_([NaN, NaN, NaN, NaN]);
+                    output.__quantized__ = true;
+                    break;
+                default:
+                    break;
+            }
+        }
+        return output;
+    }
+
+    isNativeType(obj, type, N) {
         const torch = this.torch;
         switch (type.str()) {
             case 'Tensor':
@@ -3526,15 +3475,15 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     return true;
                 }
                 return false;
-            case 'SymInt[1]':
-                return this.isNativeType(obj, torch.IntType.get()) || this.isNativeType(obj, torch.ListType.get(torch.IntType.get()));
             case 'int[]':
-            case 'int[2]':
-            case 'int[3]':
+                if (N === 1 && this.isNativeType(obj, torch.IntType.get())) {
+                    return true;
+                }
                 return (Array.isArray(obj) && obj.every((item) => this.isNativeType(item, torch.IntType.get()) || item === undefined || (item.__class__ === 'number' && isNaN(item))) ||
                     (obj instanceof torch.Value && obj.type() instanceof torch.ListType && obj.type().getElementType() instanceof torch.IntType)) ||
                     (obj instanceof torch.Value && obj.type() instanceof torch.OptionalType && obj.type().getElementType() instanceof torch.ListType && obj.type().getElementType().getElementType() instanceof torch.IntType);
-            case 'int[1]':
+            case 'SymInt[1]':
+                return this.isNativeType(obj, torch.IntType.get()) || this.isNativeType(obj, torch.ListType.get(torch.IntType.get()));
             case 'float':
                 return obj !== null && (typeof obj === 'number' || obj instanceof Number) ||
                     (pytorch.Utility.isInstance(obj, 'torch.Value') && pytorch.Utility.isInstance(obj.type(), 'torch.FloatType'));
@@ -3621,7 +3570,7 @@ pytorch.jit.Execution = class extends pytorch.Execution {
         }
     }
 
-    isType(obj, type) {
+    isType(obj, type) { // remove
         const torch = this.torch;
         switch (type) {
             case 'Tensor':
@@ -3758,6 +3707,55 @@ pytorch.jit.Execution = class extends pytorch.Execution {
         }
     }
 
+    getNativeType(value) { // rename
+        const torch = this.torch;
+        if (value === null || value === undefined) {
+            return undefined;
+        } else if (value === true || value === false) {
+            return torch.BoolType.get();
+        } else if (pytorch.Utility.isTensor(value)) {
+            return torch.TensorType.get();
+        } else if (typeof value === 'string') {
+            return torch.StringType.get();
+        } else if (Number(value) === value && value % 1 === 0) {
+            return torch.IntType.get();
+        } else if (Number(value) === value) {
+            return torch.FloatType.get();
+        } else if (Array.isArray(value) && value.every((item) => Number(item) === item && item % 1 === 0)) {
+            return torch.ListType.get(torch.IntType.get());
+        } else if (Array.isArray(value) && value.every((item) => Number(item) === item)) {
+            return torch.ListType.get(torch.FloatType.get());
+        } else if (value instanceof torch.Value) {
+            return value.type();
+        }
+        const text = (JSON.stringify(value) || '(undefined)').substring(0, 10);
+        throw new pytorch.Error(`Unsupported ops argument type '${text}'.`);
+    }
+
+    getType(value) { // remove
+        if (value === null || value === undefined) {
+            return undefined;
+        } else if (value === true || value === false) {
+            return 'boolean';
+        } else if (pytorch.Utility.isTensor(value)) {
+            return 'Tensor';
+        } else if (typeof value === 'string') {
+            return 'string';
+        } else if (Number(value) === value && value % 1 === 0) {
+            return 'int64';
+        } else if (Number(value) === value) {
+            return 'float32';
+        } else if (Array.isArray(value) && value.every((item) => Number(item) === item && item % 1 === 0)) {
+            return 'int64[]';
+        } else if (Array.isArray(value) && value.every((item) => Number(item) === item)) {
+            return 'float32[]';
+        } else if (pytorch.Utility.isInstance(value, 'torch.Value')) {
+            return pytorch.Utility.toType(value.type());
+        }
+        const text = (JSON.stringify(value) || '(undefined)').substring(0, 10);
+        throw new pytorch.Error(`Unsupported ops argument type '${text}'.`);
+    }
+
     _overload(target, name, args, context) {
         const moduleName = pytorch.Utility.target(target);
         if (!moduleName) {
@@ -3782,185 +3780,40 @@ pytorch.jit.Execution = class extends pytorch.Execution {
         } else if (type === 'complex') {
             op_name = 'aten::Complex';
         }
-        this.native = false;
-        if (this.native && op_name) {
-            const overloads = torch._C._jit_get_schemas_for_operator(op_name);
-            /*
-            if (!overloads && type.startsWith('ops.') && !type.startsWith('ops.prim')) {
-                const module = this.import(moduleName);
-                if (!module || !module[name]) {
-                    const metadata = {};
-                    metadata.name = type;
-                    metadata.inputs = [];
-                    metadata.outputs = [];
-                    for (let i = 0; i < args.length; i++) {
-                        const input = {};
-                        let argument = args[i];
-                        input.name = i.toString();
-                        if (argument.type === '=' && argument.target && argument.target.type === 'id') {
-                            input.name = this.expression(argument.target, context);
-                            argument = argument.expression;
-                        }
-                        const obj = this.expression(argument, context);
-                        input.type = pytorch.Utility.getType(obj);
-                        metadata.inputs.push(input);
-                    }
-                    const count = context.target.length > 0 ? context.target[context.target.length - 1].length : 0;
-                    for (let i = 0; i < count; i++) {
-                        metadata.outputs.push({ name: '', type: '' });
-                    }
-                    this._metadata.add(type, metadata);
-                    overloads = [metadata];
-                }
-            }
-            */
-            if (!overloads) {
-                if (type.startsWith('aten::') || type.startsWith('prim::')) {
-                    throw new pytorch.Error(`Unknown function '${type}'.`);
-                }
-                return null;
-            }
-            const evalArgs = args.map((argument) => {
-                if (argument.type === '=' && argument.target && argument.target.type === 'id') {
-                    argument = argument.expression;
-                }
-                return this.expression(argument, context);
-            });
-            const matches = [];
-            for (const schema of overloads) {
-                const parameters = schema.arguments || [];
-                let next = false;
-                let kwarg_only = false;
-                let position = 0;
-                let index = 0;
-                while (position < evalArgs.length) {
-                    if (index >= parameters.length) {
-                        next = !schema.name.startsWith('_caffe2::') && !schema.is_vararg;
-                        break;
-                    }
-                    const arg = parameters[index];
-                    if (arg.kwarg_only) {
-                        break;
-                    }
-                    index++;
-                    const value = evalArgs[position];
-                    let type = arg.real_type;
-                    let optional = false;
-                    if (type instanceof torch.OptionalType) {
-                        type = type.getElementType();
-                        optional = true;
-                    }
-                    if (optional === true &&
-                        (type instanceof torch.FloatType || type instanceof torch.BoolType || type instanceof torch.IntType || type instanceof torch.ComplexType || type.kind() === 'ScalarTypeType' || type instanceof torch.DeviceObjType || type.kind() === 'LayoutKind') &&
-                        value instanceof torch.Value && value.type() instanceof torch.NoneType) {
-                        position++;
-                    } else if (!this.isNativeType(value, type) && value !== null) {
-                        if (optional) {
-                            continue;
-                        }
-                        next = true;
-                        break;
-                    } else if (args[position].type === '=') {
-                        next = true;
-                        break;
-                    } else {
-                        position++;
-                    }
-                }
-                if (next) {
-                    continue;
-                }
-                if (args.every((arg, index) => index < position || (arg.type === '=' && arg.target && arg.target.type === 'id'))) {
-                    const params = new Map(parameters.slice(index).map((a) => [a.name, a]));
-                    while (position < args.length) {
-                        const value = evalArgs[position];
-                        const arg = params.get(args[position].target.value);
-                        position++;
-                        if (!arg) {
-                            next = true;
-                            break;
-                        }
-                        if (arg.kwarg_only) {
-                            kwarg_only = true;
-                        }
-                        let type = arg.real_type;
-                        let optional = false;
-                        if (type instanceof torch.OptionalType) {
-                            type = type.getElementType();
-                            optional = true;
-                        }
-                        if (!this.isNativeType(value, type)) {
-                            if (optional) {
-                                continue;
-                            }
-                            next = true;
-                            break;
-                        }
-                    }
-                }
-                if (next) {
-                    continue;
-                }
-                if (position < evalArgs.length && !schema.is_vararg && !schema.name.startsWith('_caffe2::')) {
-                    continue;
-                }
-                if (!kwarg_only && parameters.slice(index).some((arg) => !arg.has_default_value())) {
-                    continue;
-                }
-                matches.push(schema);
-            }
-            if (matches.length > 1) {
-                const keys = new Map([['IntType', 1], ['FloatType', 2], ['TensorType', 3], ['NumberType', 4]]);
-                matches.sort((a, b) => {
-                    let keyA = keys.get(a.arguments[0].real_type.kind()) || 5;
-                    let keyB = keys.get(b.arguments[0].real_type.kind()) || 5;
-                    if (keyA === keyB && a.arguments.length > 1 && b.arguments.length > 1) {
-                        keyA = keys.get(a.arguments[1].real_type.kind()) || 5;
-                        keyB = keys.get(b.arguments[1].real_type.kind()) || 5;
-                    }
-                    return keyA - keyB;
-                });
-            }
-            if (matches.length === 0) {
-                throw new pytorch.Error(`Unknown function '${op_name}'.`);
-            }
-            // return [matches[0], evalArgs];
-        }
-        let overloads = this._types.get(op_name);
-        if (!overloads && type.startsWith('ops.') && !type.startsWith('ops.prim')) {
+        let overloads = null;
+        let evalArgs = null;
+        overloads = torch._C._jit_get_schemas_for_operator(op_name);
+        if ((!overloads || overloads.length === 0) && type.startsWith('ops.') && !type.startsWith('ops.prim')) {
             const module = this.import(moduleName);
             if (!module || !module[name]) {
-                const metadata = {};
-                metadata.name = type;
-                metadata.inputs = [];
-                metadata.outputs = [];
+                const schema = new torch.FunctionSchema(op_name, null, [], [], false, false);
                 for (let i = 0; i < args.length; i++) {
-                    const input = {};
                     let argument = args[i];
-                    input.name = i.toString();
+                    let name = i.toString();
                     if (argument.type === '=' && argument.target && argument.target.type === 'id') {
-                        input.name = this.expression(argument.target, context);
+                        name = this.expression(argument.target, context);
                         argument = argument.expression;
                     }
                     const obj = this.expression(argument, context);
-                    input.type = pytorch.Utility.getType(obj);
-                    metadata.inputs.push(input);
+                    const real_type = this.getNativeType(obj);
+                    schema.arguments.push(new torch.Argument(name, null, real_type, null, null, false, null));
                 }
                 const count = context.target.length > 0 ? context.target[context.target.length - 1].length : 0;
                 for (let i = 0; i < count; i++) {
-                    metadata.outputs.push({ name: '', type: '' });
+                    schema.returns.push(new torch.Argument('', null, null, null, null, false, null));
                 }
-                this._metadata.add(type, metadata);
-                overloads = [metadata];
+                const op = new torch._C.Operator(schema);
+                torch._C._get_registry().registerOperator(op);
+                overloads = [schema];
             }
         }
-        if (!overloads) {
+        if (!overloads || overloads.length === 0) {
             if (type.startsWith('aten::') || type.startsWith('prim::')) {
                 throw new pytorch.Error(`Unknown function '${type}'.`);
             }
             return null;
         }
-        const evalArgs = args.map((argument) => {
+        evalArgs = args.map((argument) => {
             if (argument.type === '=' && argument.target && argument.target.type === 'id') {
                 argument = argument.expression;
             }
@@ -3968,7 +3821,7 @@ pytorch.jit.Execution = class extends pytorch.Execution {
         });
         const matches = [];
         for (const schema of overloads) {
-            const parameters = schema.inputs || [];
+            const parameters = schema.arguments || [];
             let next = false;
             let kwarg_only = false;
             let position = 0;
@@ -3983,18 +3836,18 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     break;
                 }
                 index++;
-                const value = evalArgs[position];
-                let type = arg.type;
+                const v = evalArgs[position];
+                let type = arg.real_type;
                 let optional = false;
-                if (type.endsWith('?')) {
-                    type = arg.type.substring(0, arg.type.length - 1);
+                if (type instanceof torch.OptionalType) {
+                    type = type.getElementType();
                     optional = true;
                 }
                 if (optional === true &&
-                    (type === 'float32' || type === 'boolean' || type === 'int64' || type === 'complex' || type === 'ScalarType' || type === 'Device' || type === 'Layout') &&
-                    value instanceof torch.Value && value.type() instanceof torch.NoneType) {
+                    (type instanceof torch.FloatType || type instanceof torch.BoolType || type instanceof torch.IntType || type instanceof torch.ComplexType || type.kind() === 'ScalarTypeType' || type instanceof torch.DeviceObjType || type.kind() === 'LayoutKind') &&
+                    v instanceof torch.Value && v.type() instanceof torch.NoneType) {
                     position++;
-                } else if (!this.isType(value, type) && value !== null) {
+                } else if (!this.isNativeType(v, type, arg.N) && v !== null) {
                     if (optional) {
                         continue;
                     }
@@ -4023,13 +3876,13 @@ pytorch.jit.Execution = class extends pytorch.Execution {
                     if (arg.kwarg_only) {
                         kwarg_only = true;
                     }
-                    let type = arg.type;
+                    let type = arg.real_type;
                     let optional = false;
-                    if (type.endsWith('?')) {
-                        type = arg.type.substring(0, arg.type.length - 1);
+                    if (type instanceof torch.OptionalType) {
+                        type = type.getElementType();
                         optional = true;
                     }
-                    if (!this.isType(value, type)) {
+                    if (!this.isNativeType(value, type, arg.N)) {
                         if (optional) {
                             continue;
                         }
@@ -4044,25 +3897,25 @@ pytorch.jit.Execution = class extends pytorch.Execution {
             if (position < evalArgs.length && !schema.is_vararg && !schema.name.startsWith('_caffe2::')) {
                 continue;
             }
-            if (!kwarg_only && parameters.slice(index).some((parameter) => parameter.default === undefined)) {
+            if (!kwarg_only && parameters.slice(index).some((arg) => !arg.has_default_value())) {
                 continue;
             }
             matches.push(schema);
         }
         if (matches.length > 1) {
-            const keys = new Map([['int64', 1], ['float32', 2], ['Tensor', 3], ['Scalar', 4]]);
+            const keys = new Map([['IntType', 1], ['FloatType', 2], ['TensorType', 3], ['NumberType', 4]]);
             matches.sort((a, b) => {
-                let keyA = keys.get(a.inputs[0].type) || 4;
-                let keyB = keys.get(b.inputs[0].type) || 4;
-                if (keyA === keyB && a.inputs.length > 1 && b.inputs.length > 1) {
-                    keyA = keys.get(a.inputs[1].type) || 4;
-                    keyB = keys.get(b.inputs[1].type) || 4;
+                let keyA = keys.get(a.arguments[0].real_type.kind()) || 5;
+                let keyB = keys.get(b.arguments[0].real_type.kind()) || 5;
+                if (keyA === keyB && a.arguments.length > 1 && b.arguments.length > 1) {
+                    keyA = keys.get(a.arguments[1].real_type.kind()) || 5;
+                    keyB = keys.get(b.arguments[1].real_type.kind()) || 5;
                 }
                 return keyA - keyB;
             });
         }
         if (matches.length === 0) {
-            throw new pytorch.Error(`Unknown function '${type}'.`);
+            throw new pytorch.Error(`Unknown function '${op_name}'.`);
         }
         return [matches[0], evalArgs];
     }
@@ -4894,30 +4747,6 @@ pytorch.Utility = class {
         return pytorch.Utility.isObjectType(type);
     }
 
-    static getType(value) {
-        if (value === null || value === undefined) {
-            return undefined;
-        } else if (value === true || value === false) {
-            return 'boolean';
-        } else if (pytorch.Utility.isTensor(value)) {
-            return 'Tensor';
-        } else if (typeof value === 'string') {
-            return 'string';
-        } else if (Number(value) === value && value % 1 === 0) {
-            return 'int64';
-        } else if (Number(value) === value) {
-            return 'float32';
-        } else if (Array.isArray(value) && value.every((item) => Number(item) === item && item % 1 === 0)) {
-            return 'int64[]';
-        } else if (Array.isArray(value) && value.every((item) => Number(item) === item)) {
-            return 'float32[]';
-        } else if (pytorch.Utility.isInstance(value, 'torch.Value')) {
-            return pytorch.Utility.toType(value.type());
-        }
-        const text = (JSON.stringify(value) || '(undefined)').substring(0, 10);
-        throw new pytorch.Error(`Unsupported ops argument type '${text}'.`);
-    }
-
     static isSubclass(value, name) {
         if (value && value.__module__ && value.__name__) {
             return name === `${value.__module__}.${value.__name__}`;
@@ -5573,4 +5402,6 @@ pytorch.Error = class extends Error {
     }
 };
 
+export const Metadata = pytorch.Metadata;
+export const Execution = pytorch.Execution;
 export const ModelFactory = pytorch.ModelFactory;
diff --git a/source/tf.js b/source/tf.js
index 692f5bd816..6d555107f0 100644
--- a/source/tf.js
+++ b/source/tf.js
@@ -373,22 +373,37 @@ tf.ModelFactory = class {
                 producer = 'PyTorch';
                 const openPyTorchMetadata = async (context, saved_model) => {
                     try {
+                        const pytorch = await context.require('./pytorch');
+                        const metadata_ = await pytorch.Metadata.open(context);
+                        const execution = new pytorch.Execution();
+                        execution.registerMetadata(metadata_);
+
+                        /*
                         const data = await context.request('pytorch-metadata.json');
                         const metadata = new Map();
                         for (const item of JSON.parse(data)) {
-                            const name = item.name;
+                            let name = item.name;
                             if (name.indexOf('::') !== -1) {
-                                const index = name.indexOf('.');
-                                const key = index === -1 ? name : name.substring(0, index);
-                                if (!metadata.has(key)) {
-                                    metadata.set(key, []);
+                                const brace = name.indexOf('(');
+                                name = brace === -1 ? name : name.substring(0, brace);
+                                const dot = name.indexOf('.');
+                                name = dot === -1 ? name : name.substring(0, dot);
+                                if (!metadata.has(name)) {
+                                    metadata.set(name, []);
                                 }
-                                metadata.get(key).push(item);
+                                metadata.get(name).push(item);
                             }
                         }
+                        */
+                        const torch = execution.register('torch');
                         for (const graph of saved_model.meta_graphs) {
                             for (const node of graph.graph_def.node) {
-                                node.__metadata__ = Array.from(metadata.get(node.op) || []);
+                                const schemas = torch._C._jit_get_schemas_for_operator(node.op);
+                                if (Array.isArray(schemas) && schemas.length > 0) {
+                                    node.__metadata__ = schemas;
+                                    node.__torch__ = torch;
+                                }
+                                // node.__metadata__ = Array.from(metadata.get(node.op) || []);
                             }
                         }
                     } catch {
@@ -1949,8 +1964,9 @@ tf.Context = class {
                     }
                 }
                 if (node.__metadata__) {
+                    const torch = node.__torch__;
                     const match = (node, schema) => {
-                        const args = schema.inputs || [];
+                        const args = schema.arguments || [];
                         const inputs = node.input || [];
                         if (inputs.length > args.length) {
                             return false;
@@ -1958,14 +1974,16 @@ tf.Context = class {
                         for (let i = 0; i < inputs.length; i++) {
                             const input = inputs[i];
                             const arg = args[i];
-                            switch (arg.type) {
+                            let type = arg.real_type;
+                            type = type instanceof torch.OptionalType ? type.getElementType() : type;
+                            switch (type.str()) {
                                 case 'Tensor': {
                                     if ((input.constant === undefined && input.list === undefined) || input.constant === null) {
                                         continue;
                                     }
                                     break;
                                 }
-                                case 'int64':
+                                case 'int':
                                 case 'SymInt': {
                                     if (input.constant !== undefined &&
                                         Number.isInteger(parseInt(input.constant, 10))) {
@@ -1973,14 +1991,14 @@ tf.Context = class {
                                     }
                                     break;
                                 }
-                                case 'float32': {
+                                case 'float': {
                                     if (input.constant !== undefined && !isNaN(parseFloat(input.constant))) {
                                         continue;
                                     }
                                     break;
                                 }
-                                case 'int64[]':
-                                case 'int64[2]':
+                                case 'int[]':
+                                case 'int[2]':
                                 case 'SymInt[]':
                                 case 'SymInt[2]': {
                                     if (Array.isArray(input.list)) {
@@ -1991,7 +2009,7 @@ tf.Context = class {
                                     }
                                     break;
                                 }
-                                case 'boolean': {
+                                case 'bool': {
                                     if (input.constant === 'false' ||
                                         input.constant === 'true' ||
                                         input.constant === '0' ||
@@ -2017,18 +2035,20 @@ tf.Context = class {
                     };
                     const schema = node.__metadata__.find((schema) => match(node, schema));
                     if (schema) {
-                        const args = schema.inputs || [];
+                        const args = schema.arguments;
                         const inputs = node.input || [];
                         for (let i = 0; i < inputs.length; i++) {
                             const input = inputs[i];
                             delete input.metadata;
                             const arg = args[i];
-                            switch (arg.type) {
+                            let type = arg.real_type;
+                            type = type instanceof torch.OptionalType ? type.getElementType() : type;
+                            switch (type.str()) {
                                 case 'Tensor': {
                                     input.metadata = arg;
                                     break;
                                 }
-                                case 'int64':
+                                case 'int':
                                 case 'SymInt': {
                                     const value = parseInt(input.constant, 10);
                                     input.attr = new tf.proto.tensorflow.AttrValue();
@@ -2036,15 +2056,15 @@ tf.Context = class {
                                     input.attr.metadata = arg;
                                     break;
                                 }
-                                case 'float32': {
+                                case 'float': {
                                     const value = parseFloat(input.constant, 10);
                                     input.attr = new tf.proto.tensorflow.AttrValue();
                                     input.attr.f = value;
                                     input.attr.metadata = arg;
                                     break;
                                 }
-                                case 'int64[]':
-                                case 'int64[2]':
+                                case 'int[]':
+                                case 'int[2]':
                                 case 'SymInt[]':
                                 case 'SymInt[2]': {
                                     const list = input.list.map((item) => parseInt(item, 10));
@@ -2054,7 +2074,7 @@ tf.Context = class {
                                     input.attr.metadata = arg;
                                     break;
                                 }
-                                case 'boolean': {
+                                case 'bool': {
                                     input.attr = new tf.proto.tensorflow.AttrValue();
                                     input.attr.b = input.constant === 'true' || input.constant === '1';
                                     input.attr.metadata = arg;
diff --git a/test/models.json b/test/models.json
index ce6c1da9eb..912142e303 100644
--- a/test/models.json
+++ b/test/models.json
@@ -5844,7 +5844,7 @@
     "type":     "pytorch",
     "target":   "pyg_model.pt",
     "source":   "https://github.com/lutzroeder/netron/files/10369483/pyg_model.zip[pyg_model.pt]",
-    "error":    "Unknown function 'torch.linear'.",
+    "error":    "Unknown function 'aten::linear'.",
     "link":     "https://github.com/lutzroeder/netron/issues/546"
   },
   {
diff --git a/tools/pytorch_script.py b/tools/pytorch_script.py
index 2e3647cbd7..9770c78a5f 100644
--- a/tools/pytorch_script.py
+++ b/tools/pytorch_script.py
@@ -16,365 +16,6 @@
 metadata_file = os.path.join(source_dir, 'pytorch-metadata.json')
 pytorch_source_dir = os.path.join(third_party_dir, 'source', 'pytorch')
 
-class Metadata: # pylint: disable=too-few-public-methods,missing-class-docstring
-
-    def __init__(self, metadata):
-        self.types = metadata
-        self.cache = set()
-        self._primitives = {
-            'int': 'int64', 'float': 'float32', 'bool': 'boolean', 'str': 'string'
-        }
-
-    def type(self, schema): # pylint: disable=missing-function-docstring
-        key = schema.name if isinstance(schema, Schema) else schema.split('(', 1)[0].strip()
-        if key not in self.cache:
-            self.cache.add(key)
-            schema = schema if isinstance(schema, Schema) else Schema(schema)
-            arguments = list(filter(lambda _: \
-                not(_.kwarg_only and hasattr(_, 'alias')), schema.arguments))
-            returns = schema.returns
-            value = self.types.setdefault(schema.name, { 'name': schema.name, })
-            inputs = value.get('inputs', [])
-            outputs = value.get('outputs', [])
-            inputs = [ inputs[i] if i < len(inputs) else {} for i in range(len(arguments)) ]
-            outputs = [ outputs[i] if i < len(outputs) else {} for i in range(len(returns)) ]
-            value['inputs'] = inputs
-            value['outputs'] = outputs
-            for i, _ in enumerate(arguments):
-                argument = inputs[i]
-                argument['name'] = _.name
-                self._argument(argument, getattr(_, 'type'))
-                if hasattr(_, 'default'):
-                    argument['default'] = _.default
-                if hasattr(_, 'kwarg_only') and _.kwarg_only is True:
-                    argument['kwarg_only'] = True
-            for i, _ in enumerate(returns):
-                argument = outputs[i]
-                if hasattr(_, 'name'):
-                    argument['name'] = _.name
-                self._argument(argument, getattr(_, 'type'))
-        return self.types[key]
-
-    def _argument_type(self, value):
-        if isinstance(value, Schema.OptionalType):
-            element_type = self._argument_type(value.element_type)
-            return f'{element_type}?'
-        if isinstance(value, Schema.ListType):
-            element_type = self._argument_type(value.element_type)
-            size = str(value.size) if hasattr(value, 'size') else ''
-            return f'{element_type}[{size}]'
-        if isinstance(value, Schema.DictType):
-            key_type = self._argument_type(value.getKeyType())
-            value_type = self._argument_type(value.getValueType())
-            return f'Dict({key_type}, {value_type})'
-        if isinstance(value, Schema.TupleType):
-            elements = []
-            for element in value.elements():
-                elements.append(self._argument_type(element))
-            return f'({', '.join(elements)})'
-        name = value.name
-        return self._primitives[name] if name in self._primitives else name
-
-    def _argument(self, argument, value):
-        argument_type = self._argument_type(value)
-        if argument_type:
-            argument['type'] = argument_type
-        else:
-            argument.pop('type', None)
-        if 'optional' in argument:
-            del argument['optional']
-
-class Schema: # pylint: disable=too-few-public-methods,missing-class-docstring
-    def __init__(self, value):
-        self.value = value
-        lexer = Schema.Lexer(value)
-        lexer.whitespace(0)
-        self._parse_name(lexer)
-        lexer.whitespace(0)
-        if lexer.kind == '(':
-            self._parse_arguments(lexer)
-            lexer.whitespace(0)
-            lexer.expect('->')
-            lexer.whitespace(0)
-            self._parse_returns(lexer)
-    def __str__(self):
-        arguments = []
-        kwarg_only = False
-        for _ in self.arguments:
-            if not kwarg_only and _.kwarg_only:
-                kwarg_only = True
-                arguments.append('*')
-            arguments.append(_.__str__())
-        if self.is_vararg:
-            arguments.append('...')
-        returns = ', '.join(map(lambda _: _.__str__(), self.returns))
-        returns = returns if len(self.returns) == 1 else '(' + returns + ')'
-        return self.name + '(' + ', '.join(arguments) + ') -> ' + returns
-    def _parse_name(self, lexer):
-        self.name = lexer.expect('id')
-        if lexer.eat(':'):
-            lexer.expect(':')
-            self.name = self.name + '::' + lexer.expect('id')
-        if lexer.eat('.'):
-            self.name = self.name + '.' + lexer.expect('id')
-    def _parse_arguments(self, lexer):
-        self.arguments = []
-        self.is_vararg = False
-        self.kwarg_only = False
-        lexer.expect('(')
-        if not lexer.eat(')'):
-            while True:
-                lexer.whitespace(0)
-                if self.is_vararg:
-                    raise NotImplementedError()
-                if lexer.eat('*'):
-                    self.kwarg_only = True
-                elif lexer.eat('...'):
-                    self.is_vararg = True
-                else:
-                    self.arguments.append(Schema.Argument(lexer, False, self.kwarg_only))
-                lexer.whitespace(0)
-                if not lexer.eat(','):
-                    break
-            lexer.expect(')')
-    def _parse_returns(self, lexer):
-        self.returns = []
-        self.is_varret = False
-        if lexer.eat('...'):
-            self.is_varret = True
-        elif lexer.eat('('):
-            lexer.whitespace(0)
-            if not lexer.eat(')'):
-                while True:
-                    lexer.whitespace(0)
-                    if self.is_varret:
-                        raise NotImplementedError()
-                    if lexer.eat('...'):
-                        self.is_varret = True
-                    else:
-                        self.returns.append(Schema.Argument(lexer, True, False))
-                    lexer.whitespace(0)
-                    if not lexer.eat(','):
-                        break
-                lexer.expect(')')
-            lexer.whitespace(0)
-        else:
-            self.returns.append(Schema.Argument(lexer, True, False))
-    class Argument: # pylint: disable=too-few-public-methods
-        def __init__(self, lexer, is_return, kwarg_only):
-            value = Schema.Type.parse(lexer)
-            lexer.whitespace(0)
-            while True:
-                if lexer.eat('['):
-                    size = None
-                    if lexer.kind == '#':
-                        size = int(lexer.value)
-                        lexer.next()
-                    lexer.expect(']')
-                    value = Schema.ListType(value, size)
-                elif lexer.eat('?'):
-                    value = Schema.OptionalType(value)
-                elif lexer.kind == '(' and not hasattr(self, 'alias'):
-                    self.alias = self._parse_alias(lexer)
-                else:
-                    break
-            self.type = value
-            if is_return:
-                lexer.whitespace(0)
-                self.kwarg_only = False
-                if lexer.kind == 'id':
-                    self.name = lexer.expect('id')
-            else:
-                lexer.whitespace(1)
-                self.kwarg_only = kwarg_only
-                self.name = lexer.expect('id')
-                lexer.whitespace(0)
-                if lexer.eat('='):
-                    lexer.whitespace(0)
-                    self.default = self._parse_value(lexer)
-        def __str__(self):
-            alias = '(' + self.alias + ')' if hasattr(self, 'alias') else ''
-            name = ' ' + self.name if hasattr(self, 'name') else ''
-            default = '=' + self.default.__str__() if hasattr(self, 'default') else ''
-            return self.type.__str__() + alias + name + default
-        def _parse_value(self, lexer):
-            if lexer.kind == 'id':
-                if lexer.value in ('True', 'False'):
-                    value = bool(lexer.value == 'True')
-                elif lexer.value == 'None':
-                    value = None
-                elif lexer.value in ('Mean', 'contiguous_format', 'long'):
-                    value = lexer.value
-                else:
-                    raise NotImplementedError()
-            elif lexer.kind == '#':
-                value = float(lexer.value) if \
-                    lexer.value.find('.') != -1 or lexer.value.find('e') != -1 else \
-                    int(lexer.value)
-            elif lexer.kind == 'string':
-                value = lexer.value[1:-1]
-            elif lexer.eat('['):
-                value = []
-                if not lexer.eat(']'):
-                    while True:
-                        lexer.whitespace(0)
-                        value.append(self._parse_value(lexer))
-                        lexer.whitespace(0)
-                        if not lexer.eat(','):
-                            break
-                    lexer.expect(']')
-                return value
-            else:
-                raise NotImplementedError()
-            lexer.next()
-            return value
-        def _parse_alias(self, lexer):
-            value = ''
-            lexer.expect('(')
-            while not lexer.eat(')'):
-                value += lexer.value
-                lexer.next()
-            return value
-    class Type: # pylint: disable=too-few-public-methods,missing-class-docstring
-        def __init__(self, name):
-            self.name = name
-        def __str__(self):
-            return self.name
-        @staticmethod
-        def parse(lexer): # pylint: disable=missing-function-docstring
-            if lexer.eat('('):
-                lexer.whitespace(0)
-                elements = []
-                while not lexer.eat(')'):
-                    elements.append(Schema.Type.parse(lexer))
-                    lexer.whitespace(0)
-                    lexer.eat(',')
-                    lexer.whitespace(0)
-                return Schema.TupleType(elements)
-            name = lexer.expect('id')
-            while lexer.eat('.'):
-                name = name + '.' + lexer.expect('id')
-            if name == 'Dict':
-                lexer.expect('(')
-                lexer.whitespace(0)
-                key_type = Schema.Type.parse(lexer)
-                lexer.whitespace(0)
-                lexer.expect(',')
-                lexer.whitespace(0)
-                value_type = Schema.Type.parse(lexer)
-                lexer.whitespace(0)
-                lexer.expect(')')
-                return Schema.DictType(key_type, value_type)
-            if name == 'Future':
-                lexer.expect('(')
-                lexer.whitespace(0)
-                elem_type = Schema.Type.parse(lexer)
-                lexer.whitespace(0)
-                lexer.expect(')')
-                return Schema.Type(f'Future({elem_type})')
-            return Schema.Type(name)
-    class OptionalType: # pylint: disable=too-few-public-methods,missing-class-docstring
-        def __init__(self, element_type):
-            self.element_type = element_type
-        def __str__(self):
-            return self.element_type.__str__() + '?'
-    class ListType: # pylint: disable=too-few-public-methods,missing-class-docstring
-        def __init__(self, element_type, size):
-            self.element_type = element_type
-            if size:
-                self.size = size
-        def __str__(self):
-            size = self.size.__str__() if hasattr(self, 'size') else ''
-            return self.element_type.__str__() + '[' + size + ']'
-    class DictType:
-        def __init__(self, key_type, value_type):
-            self._key_type = key_type
-            self._value_type = value_type
-        def __str__(self):
-            return 'Dict(' + str(self._key_type) + ', ' + str(self._value_type) + ')'
-        def getKeyType(self): # pylint: disable=invalid-name,missing-function-docstring
-            return self._key_type
-        def getValueType(self): # pylint: disable=invalid-name,,missing-function-docstring
-            return self._value_type
-    class TupleType:
-        def __init__(self, elements):
-            self._elements = elements
-        def elements(self): # pylint: disable=invalid-name,,missing-function-docstring
-            return self._elements
-    class Lexer: # pylint: disable=too-few-public-methods,missing-class-docstring
-        def __init__(self, buffer):
-            self.buffer = buffer
-            self.position = 0
-            self.value = ''
-            self.next()
-        def eat(self, kind): # pylint: disable=missing-function-docstring
-            if self.kind != kind:
-                return None
-            value = self.value
-            self.next()
-            return value
-        def expect(self, kind): # pylint: disable=missing-function-docstring
-            if self.kind != kind:
-                raise SyntaxError("Unexpected '" + self.kind + "' instead of '" + kind + "'.")
-            value = self.value
-            self.next()
-            return value
-        def whitespace(self, count): # pylint: disable=missing-function-docstring
-            if self.kind != ' ':
-                if count > len(self.value):
-                    raise IndexError()
-                return False
-            self.next()
-            return True
-        def next(self): # pylint: disable=missing-function-docstring,too-many-branches
-            self.position += len(self.value)
-            i = self.position
-            if i >= len(self.buffer):
-                self.kind = '\0'
-                self.value = ''
-            elif self.buffer[i] == ' ':
-                while self.buffer[i] == ' ':
-                    i += 1
-                self.kind = ' '
-                self.value = self.buffer[self.position:i]
-            elif self.buffer[i] == '.' and self.buffer[i+1] == '.' and self.buffer[i+2] == '.':
-                self.kind = '...'
-                self.value = '...'
-            elif self.buffer[i] in ('(', ')', ':', '.', '[', ']', ',', '=', '?', '!', '*', '|'):
-                self.kind = self.buffer[i]
-                self.value = self.buffer[i]
-            elif (self.buffer[i] >= 'a' and self.buffer[i] <= 'z') or \
-                 (self.buffer[i] >= 'A' and self.buffer[i] <= 'Z') or self.buffer[i] == '_':
-                i += 1
-                while i < len(self.buffer) and \
-                    ((self.buffer[i] >= 'a' and self.buffer[i] <= 'z') or \
-                     (self.buffer[i] >= 'A' and self.buffer[i] <= 'Z') or \
-                     (self.buffer[i] >= '0' and self.buffer[i] <= '9') or self.buffer[i] == '_'):
-                    i += 1
-                self.kind = 'id'
-                self.value = self.buffer[self.position:i]
-            elif self.buffer[i] == '-' and self.buffer[i+1] == '>':
-                self.kind = '->'
-                self.value = '->'
-            elif (self.buffer[i] >= '0' and self.buffer[i] <= '9') or self.buffer[i] == '-':
-                i += 1
-                while i < len(self.buffer) and \
-                    ((self.buffer[i] >= '0' and self.buffer[i] <= '9') or \
-                    self.buffer[i] == '.' or self.buffer[i] == 'e' or self.buffer[i] == '-'):
-                    i += 1
-                self.kind = '#'
-                self.value = self.buffer[self.position:i]
-            elif self.buffer[i] in ("'", '"'):
-                quote = self.buffer[i]
-                i += 1
-                while i < len(self.buffer) and self.buffer[i] != quote:
-                    i += 2 if self.buffer[i] == '\\' and self.buffer[i+1] in ("'", '"', '\\') else 1
-                i += 1
-                self.kind = 'string'
-                self.value = self.buffer[self.position:i]
-            else:
-                raise NotImplementedError("Unsupported token at " + self.position)
-
 def _read(path):
     with open(path, 'r', encoding='utf-8') as file:
         return file.read()
@@ -425,540 +66,6 @@ def _write_metadata(value):
 ]
 
 # pylint: disable=line-too-long
-known_schema_definitions = [
-    'aten::__and__.bool(bool a, bool b) -> bool',
-    'aten::__and__.int(int a, int b) -> int',
-    'aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::__contains__.Tensor(Dict(Tensor, t) dict, Tensor key) -> bool',
-    'aten::__contains__.bool(Dict(bool, t) dict, bool key) -> bool',
-    'aten::__contains__.complex(Dict(complex, t) dict, complex key) -> bool',
-    'aten::__contains__.float(Dict(float, t) dict, float key) -> bool',
-    'aten::__contains__.float_list(float[] l, float item) -> bool',
-    'aten::__contains__.int(Dict(int, t) dict, int key) -> bool',
-    'aten::__contains__.int_list(int[] l, int item) -> bool',
-    'aten::__contains__.str(Dict(str, t) dict, str key) -> bool',
-    'aten::__contains__.str_list(str[] l, str item) -> bool',
-    'aten::__getitem__.Dict_bool(Dict(bool, t) self, bool key) -> t(*)',
-    'aten::__getitem__.Dict_complex(Dict(complex, t) self, complex key) -> t(*)',
-    'aten::__getitem__.Dict_float(Dict(float, t) self, float key) -> t(*)',
-    'aten::__getitem__.Dict_int(Dict(int, t) self, int key) -> t(*)',
-    'aten::__getitem__.Dict_str(Dict(str, t) self, str key) -> t(*)',
-    'aten::__getitem__.Dict_Tensor(Dict(Tensor, t) self, Tensor key) -> t(*)',
-    'aten::__getitem__.str(str s, int index) -> str',
-    'aten::__getitem__.t(t[](a) list, int idx) -> t(*)',
-    'aten::__is__(t1 self, t2 obj) -> bool',
-    'aten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)',
-    'aten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)',
-    'aten::_native_batch_norm_legit.no_stats_out(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!))',
-    'aten::_native_batch_norm_legit.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd) -> (Tensor(d!), Tensor(e!), Tensor(f!))',
-    'aten::_native_batch_norm_legit_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)',
-    'aten::_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor)',
-    'aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))',
-    'aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor)',
-    'aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))',
-    'aten::_set_item.Tensor(Dict(Tensor, t)(a!) l, Tensor(b -> *) idx, t(c -> *) v) -> ()',
-    'aten::_set_item.bool(Dict(bool, t)(a!) l, bool(b -> *) idx, t(c -> *) v) -> ()',
-    'aten::_set_item.complex(Dict(complex, t)(a!) l, complex(b -> *) idx, t(c -> *) v) -> ()',
-    'aten::_set_item.float(Dict(float, t)(a!) l, float(b -> *) idx, t(c -> *) v) -> ()',
-    'aten::_set_item.int(Dict(int, t)(a!) l, int(b -> *) idx, t(c -> *) v) -> ()',
-    'aten::_set_item.str(Dict(str, t)(a!) l, str(b -> *) idx, t(c -> *) v) -> ()',
-    'aten::_set_item.t(t[](a!) l, int idx, t(b -> *) el) -> t[](a!)',
-    'aten::add(Scalar a, Scalar b) -> Scalar',
-    'aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor',
-    'aten::add.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor',
-    'aten::add.complex(complex a, complex b) -> complex',
-    'aten::add.complex_float(complex a, float b) -> complex',
-    'aten::add.complex_int(complex a, int b) -> complex',
-    'aten::add.float(float a, float b) -> float',
-    'aten::add.float_complex(float a, complex b) -> complex',
-    'aten::add.float_int(float a, int b) -> float',
-    'aten::add.int(int a, int b) -> int',
-    'aten::add.int_complex(int a, complex b) -> complex',
-    'aten::add.int_float(int a, float b) -> float',
-    'aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)',
-    'aten::add.str(str a, str b) -> str',
-    'aten::add.t(t[] a, t[] b) -> t[]',
-    'aten::add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)',
-    'aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)',
-    'aten::add_.t(t[](a!) self, t[] b) -> t[]',
-    'aten::any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::any.bool(bool[] self) -> bool',
-    'aten::any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor',
-    'aten::any.dimname_out(Tensor self, str dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::any.dimname(Tensor self, str dim, bool keepdim=False) -> Tensor',
-    'aten::any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::any.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor',
-    'aten::any.float(float[] self) -> bool',
-    'aten::any.int(int[] self) -> bool',
-    'aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::any.str(str[] self) -> bool',
-    'aten::any(Tensor self) -> Tensor',
-    'aten::as_tensor.bool(bool t, *, ScalarType? dtype=None, Device? device=None) -> Tensor',
-    'aten::as_tensor.complex(complex t, *, ScalarType? dtype=None, Device? device=None) -> Tensor',
-    'aten::as_tensor.float(float t, *, ScalarType? dtype=None, Device? device=None) -> Tensor',
-    'aten::as_tensor.int(int t, *, ScalarType? dtype=None, Device? device=None) -> Tensor',
-    'aten::as_tensor.list(t[] data, *, ScalarType? dtype=None, Device? device=None) -> Tensor',
-    'aten::as_tensor(Tensor(a) data, *, ScalarType? dtype=None, Device? device=None) -> Tensor(b|a)',
-    'aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor',
-    'aten::bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_and_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::bitwise_and_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::bitwise_left_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor',
-    'aten::bitwise_left_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_left_shift.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_left_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_left_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::bitwise_left_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::bitwise_not(Tensor self) -> Tensor',
-    'aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_not_(Tensor(a!) self) -> Tensor(a!)',
-    'aten::bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_or.Scalar_Tensor(Scalar self, Tensor other) -> Tensor',
-    'aten::bitwise_or.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_or_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::bitwise_or_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::bitwise_right_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor',
-    'aten::bitwise_right_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_right_shift.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_right_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_right_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::bitwise_right_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_xor.Scalar_Tensor(Scalar self, Tensor other) -> Tensor',
-    'aten::bitwise_xor.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::bitwise_xor_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::bitwise_xor_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::Bool.float(float a) -> bool',
-    'aten::Bool.int(int a) -> bool',
-    'aten::Bool.Tensor(Tensor a) -> bool',
-    'aten::ceil.float(float a) -> int',
-    'aten::ceil.int(int a) -> int',
-    'aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::ceil.Scalar(Scalar a) -> Scalar',
-    'aten::ceil(Tensor self) -> Tensor',
-    'aten::complex(Tensor real, Tensor imag) -> Tensor',
-    'aten::Complex.bool_bool(bool x, bool y) -> complex',
-    'aten::Complex.bool_float(bool x, float y) -> complex',
-    'aten::Complex.bool_int(bool x, int y) -> complex',
-    'aten::Complex.bool_Tensor(bool x, Tensor y) -> complex',
-    'aten::Complex.float_bool(float x, bool y) -> complex',
-    'aten::Complex.float_float(float x, float y) -> complex',
-    'aten::Complex.float_int(float x, int y) -> complex',
-    'aten::Complex.float_Tensor(float x, Tensor y) -> complex',
-    'aten::Complex.int_bool(int x, bool y) -> complex',
-    'aten::Complex.int_float(int x, float y) -> complex',
-    'aten::Complex.int_int(int x, int y) -> complex',
-    'aten::Complex.int_Tensor(int x, Tensor y) -> complex',
-    'aten::complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::Complex.Scalar(Scalar a) -> complex',
-    'aten::Complex.Tensor_bool(Tensor x, bool y) -> complex',
-    'aten::Complex.Tensor_float(Tensor x, float y) -> complex',
-    'aten::Complex.Tensor_int(Tensor x, int y) -> complex',
-    'aten::Complex.Tensor_Tensor(Tensor a, Tensor b) -> complex',
-    'aten::ComplexImplicit(Tensor a) -> complex',
-    'aten::device(str a) -> Device',
-    'aten::device.with_index(str type, int index) -> Device',
-    'aten::dict.bool((bool, tVal)[] inputs) -> Dict(bool, tVal)',
-    'aten::dict.complex((complex, tVal)[] inputs) -> Dict(complex, tVal)',
-    'aten::dict.Dict_bool(Dict(bool, t)(a) self) -> Dict(bool, t)',
-    'aten::dict.Dict_complex(Dict(complex, t)(a) self) -> Dict(complex, t)',
-    'aten::dict.Dict_float(Dict(float, t)(a) self) -> Dict(float, t)',
-    'aten::dict.Dict_int(Dict(int, t)(a) self) -> Dict(int, t)',
-    'aten::dict.Dict_str(Dict(str, t)(a) self) -> Dict(str, t)',
-    'aten::dict.Dict_Tensor(Dict(Tensor, t)(a) self) -> Dict(Tensor, t)',
-    'aten::dict.float((float, tVal)[] inputs) -> Dict(float, tVal)',
-    'aten::dict.int((int, tVal)[] inputs) -> Dict(int, tVal)',
-    'aten::dict.str((str, tVal)[] inputs) -> Dict(str, tVal)',
-    'aten::dict.Tensor((Tensor, tVal)[] inputs) -> Dict(Tensor, tVal)',
-    'aten::dict() -> Dict(str, Tensor)',
-    'aten::div.complex(complex a, complex b) -> complex',
-    'aten::div.float(float a, float b) -> float',
-    'aten::div.int(int a, int b) -> float',
-    'aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)',
-    'aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)',
-    'aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor',
-    'aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::div.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor',
-    'aten::div.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::div(Scalar a, Scalar b) -> float',
-    'aten::eq_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::eq_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::eq.bool_list(bool[] a, bool[] b) -> bool',
-    'aten::eq.bool(bool a, bool b) -> bool',
-    'aten::eq.complex_float(complex a, float b) -> bool',
-    'aten::eq.complex(complex a, complex b) -> bool',
-    'aten::eq.device(Device a, Device b) -> bool',
-    'aten::eq.enum(AnyEnumType a, AnyEnumType b) -> bool',
-    'aten::eq.float_complex(float a, complex b) -> bool',
-    'aten::eq.float_int(float a, int b) -> bool',
-    'aten::eq.float_list(float[] a, float[] b) -> bool',
-    'aten::eq.float(float a, float b) -> bool',
-    'aten::eq.int_float(int a, float b) -> bool',
-    'aten::eq.int_list(int[] a, int[] b) -> bool',
-    'aten::eq.int(int a, int b) -> bool',
-    'aten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::eq.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::eq.str_list(str[] a, str[] b) -> bool',
-    'aten::eq.str(str a, str b) -> bool',
-    'aten::eq.Tensor_list(Tensor[] a, Tensor[] b) -> bool',
-    'aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::eq.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::eq(Scalar a, Scalar b) -> bool',
-    'aten::equal(Tensor self, Tensor other) -> bool',
-    'aten::extend.t(t[](a!) self, t[] other) -> ()',
-    'aten::Float.bool(bool a) -> float',
-    'aten::Float.int(int a) -> float',
-    'aten::Float.Scalar(Scalar a) -> float',
-    'aten::Float.str(str a) -> float',
-    'aten::Float.Tensor(Tensor a) -> float',
-    'aten::floor(Tensor self) -> Tensor',
-    'aten::floor.Scalar(Scalar a) -> Scalar',
-    'aten::floor.float(float a) -> int',
-    'aten::floor.int(int a) -> int',
-    'aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::floor_(Tensor(a!) self) -> Tensor(a!)',
-    'aten::floor_divide(Tensor self, Tensor other) -> Tensor',
-    'aten::floor_divide.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::floor_divide.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::floor_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::floor_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::floordiv(Scalar a, Scalar b) -> Scalar',
-    'aten::floordiv.float(float a, float b) -> float',
-    'aten::floordiv.float_int(float a, int b) -> float',
-    'aten::floordiv.int(int a, int b) -> int',
-    'aten::floordiv.int_float(int a, float b) -> float',
-    'aten::fmax(Tensor self, Tensor other) -> Tensor',
-    'aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::fmin(Tensor self, Tensor other) -> Tensor',
-    'aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::fmod(Scalar a, Scalar b) -> float',
-    'aten::fmod.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::fmod.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::fmod.float(float a, float b) -> float',
-    'aten::fmod.float_int(float a, int b) -> float',
-    'aten::fmod.int(int a, int b) -> float',
-    'aten::fmod.int_float(int a, float b) -> float',
-    'aten::fmod_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::fmod_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::get.bool(Dict(bool, t) self, bool key) -> t(*)?',
-    'aten::get.complex(Dict(complex, t) self, complex key) -> t(*)?',
-    'aten::get.default_bool(Dict(bool, t) self, bool key, t default_value) -> t(*)',
-    'aten::get.default_complex(Dict(complex, t) self, complex key, t default_value) -> t(*)',
-    'aten::get.default_float(Dict(float, t) self, float key, t default_value) -> t(*)',
-    'aten::get.default_int(Dict(int, t) self, int key, t default_value) -> t(*)',
-    'aten::get.default_str(Dict(str, t) self, str key, t default_value) -> t(*)',
-    'aten::get.default_Tensor(Dict(Tensor, t) self, Tensor key, t default_value) -> t(*)',
-    'aten::get.float(Dict(float, t) self, float key) -> t(*)?',
-    'aten::get.int(Dict(int, t) self, int key) -> t(*)?',
-    'aten::get.str(Dict(str, t) self, str key) -> t(*)?',
-    'aten::get.Tensor(Dict(Tensor, t) self, Tensor key) -> t(*)?',
-    'aten::gt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::gt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::gt.float_int(float a, int b) -> bool',
-    'aten::gt.float(float a, float b) -> bool',
-    'aten::gt.int_float(int a, float b) -> bool',
-    'aten::gt.int(int a, int b) -> bool',
-    'aten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::gt.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::gt.str(str a, str b) -> bool',
-    'aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::gt.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::gt(Scalar a, Scalar b) -> bool',
-    'aten::Int.bool(bool a) -> int',
-    'aten::Int.float(float a) -> int',
-    'aten::Int.Scalar(Scalar a) -> int',
-    'aten::Int.str(str a) -> int',
-    'aten::Int.Tensor(Tensor a) -> int',
-    'aten::int_repr(Tensor self) -> Tensor',
-    'aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::IntImplicit(Tensor a) -> int',
-    'aten::inverse(Tensor self) -> Tensor',
-    'aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::item(Tensor self) -> Scalar',
-    'aten::items.bool(Dict(bool, t) self) -> ((bool, t)[])',
-    'aten::items.complex(Dict(complex, t) self) -> ((complex, t)[])',
-    'aten::items.float(Dict(float, t) self) -> ((float, t)[])',
-    'aten::items.int(Dict(int, t) self) -> ((int, t)[])',
-    'aten::items.str(Dict(str, t) self) -> ((str, t)[])',
-    'aten::items.Tensor(Dict(Tensor, t) self) -> ((Tensor, t)[])',
-    'aten::keys.bool(Dict(bool, t) self) -> bool[](*)',
-    'aten::keys.complex(Dict(complex, t) self) -> complex[](*)',
-    'aten::keys.float(Dict(float, t) self) -> float[](*)',
-    'aten::keys.int(Dict(int, t) self) -> int[](*)',
-    'aten::keys.str(Dict(str, t) self) -> str[](*)',
-    'aten::keys.Tensor(Dict(Tensor, t) self) -> Tensor[](*)',
-    'aten::le_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::le_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::le.float_int(float a, int b) -> bool',
-    'aten::le.float(float a, float b) -> bool',
-    'aten::le.int_float(int a, float b) -> bool',
-    'aten::le.int(int a, int b) -> bool',
-    'aten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::le.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::le.str(str a, str b) -> bool',
-    'aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::le.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::le(Scalar a, Scalar b) -> bool',
-    'aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor',
-    'aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!)',
-    'aten::leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result) -> Tensor',
-    'aten::leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!)',
-    'aten::len.any(Any[] a) -> int',
-    'aten::len.Dict_bool(Dict(bool, t) self) -> int',
-    'aten::len.Dict_complex(Dict(complex, t) self) -> int',
-    'aten::len.Dict_float(Dict(float, t) self) -> int',
-    'aten::len.Dict_int(Dict(int, t) self) -> int',
-    'aten::len.Dict_str(Dict(str, t) self) -> int',
-    'aten::len.Dict_Tensor(Dict(Tensor, t) self) -> int',
-    'aten::len.str(str s) -> int',
-    'aten::len.t(t[] a) -> int',
-    'aten::len.Tensor(Tensor t) -> int',
-    'aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor',
-    'aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor',
-    'aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!)',
-    'aten::lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!)',
-    'aten::less.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::less.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::less_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::less_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::less_equal.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::less_equal.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::less_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::less_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::log10.complex(complex a) -> complex',
-    'aten::log10.float(float a) -> float',
-    'aten::log10.int(int a) -> float',
-    'aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::log10.Scalar(Scalar a) -> Scalar',
-    'aten::log10(Tensor self) -> Tensor',
-    'aten::lt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::lt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::lt.float_int(float a, int b) -> bool',
-    'aten::lt.float(float a, float b) -> bool',
-    'aten::lt.int_float(int a, float b) -> bool',
-    'aten::lt.int(int a, int b) -> bool',
-    'aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::lt.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::lt.str(str a, str b) -> bool',
-    'aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::lt.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::lt(Scalar a, Scalar b) -> bool',
-    'aten::mul(Scalar a, Scalar b) -> Scalar',
-    'aten::mul.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::mul.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::mul.complex(complex a, complex b) -> complex',
-    'aten::mul.complex_float(complex a, float b) -> complex',
-    'aten::mul.complex_int(complex a, int b) -> complex',
-    'aten::mul.float(float a, float b) -> float',
-    'aten::mul.float_complex(float a, complex b) -> complex',
-    'aten::mul.float_int(float a, int b) -> float',
-    'aten::mul.int(int a, int b) -> int',
-    'aten::mul.int_complex(int a, complex b) -> complex',
-    'aten::mul.int_float(int a, float b) -> float',
-    'aten::mul.left_t(t[] l, int n) -> t[]',
-    'aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::mul.right_(int n, t[] l) -> t[]',
-    'aten::mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::mul_.t(t[](a!) l, int n) -> t[](a!)',
-    'aten::ne(Scalar a, Scalar b) -> bool',
-    'aten::ne.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::ne.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::ne.Tensor_list(Tensor[] a, Tensor[] b) -> bool',
-    'aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::ne.bool(bool a, bool b) -> bool',
-    'aten::ne.bool_list(bool[] a, bool[] b) -> bool',
-    'aten::ne.complex(complex a, complex b) -> bool',
-    'aten::ne.complex_float(complex a, float b) -> bool',
-    'aten::ne.device(Device a, Device b) -> bool',
-    'aten::ne.enum(AnyEnumType a, AnyEnumType b) -> bool',
-    'aten::ne.float(float a, float b) -> bool',
-    'aten::ne.float_complex(float a, complex b) -> bool',
-    'aten::ne.float_int(float a, int b) -> bool',
-    'aten::ne.float_list(float[] a, float[] b) -> bool',
-    'aten::ne.int(int a, int b) -> bool',
-    'aten::ne.int_float(int a, float b) -> bool',
-    'aten::ne.int_list(int[] a, int[] b) -> bool',
-    'aten::ne.str(str a, str b) -> bool',
-    'aten::ne.str_list(str[] a, str[] b) -> bool',
-    'aten::ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)',
-    'aten::ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)',
-    'aten::neg(Tensor self) -> Tensor',
-    'aten::neg.Scalar(Scalar a) -> Scalar',
-    'aten::neg.complex(complex a) -> complex',
-    'aten::neg.float(float a) -> float',
-    'aten::neg.int(int a) -> int',
-    'aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::neg_(Tensor(a!) self) -> Tensor(a!)',
-    'aten::negative(Tensor self) -> Tensor',
-    'aten::negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::negative_(Tensor(a!) self) -> Tensor(a!)',
-    'aten::pow.complex(complex a, complex b) -> complex',
-    'aten::pow.complex_float(complex a, float b) -> complex',
-    'aten::pow.float(float a, float b) -> float',
-    'aten::pow.float_complex(float a, complex b) -> complex',
-    'aten::pow.float_int(float a, int b) -> float',
-    'aten::pow.int(int a, int b) -> float',
-    'aten::pow.int_float(int a, float b) -> float',
-    'aten::pow.int_to_int(int a, int b) -> int',
-    'aten::pow.Scalar(Scalar self, Tensor exponent) -> Tensor',
-    'aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::pow.Scalar_Scalar(Scalar a, Scalar b) -> float',
-    'aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor',
-    'aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor',
-    'aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)',
-    'aten::pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)',
-    'aten::remainder.float_int(float a, int b) -> float',
-    'aten::remainder.float(float a, float b) -> float',
-    'aten::remainder.int_float(int a, float b) -> float',
-    'aten::remainder.int(int a, int b) -> int',
-    'aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::remainder.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::remainder.Scalar_Tensor(Scalar self, Tensor other) -> Tensor',
-    'aten::remainder.Scalar(Tensor self, Scalar other) -> Tensor',
-    'aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::remainder.Tensor(Tensor self, Tensor other) -> Tensor',
-    'aten::remainder(Scalar a, Scalar b) -> Scalar',
-    'aten::replace(str self, str old, str new, int max=-1) -> str',
-    'aten::ScalarImplicit(Tensor a) -> Scalar',
-    'aten::searchsorted.Scalar_out(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!)', 
-    'aten::searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor', 
-    'aten::searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!)', 
-    'aten::searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor', 
-    'aten::sqrt.complex(complex a) -> complex',
-    'aten::sqrt.float(float a) -> float',
-    'aten::sqrt.int(int a) -> float',
-    'aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::sqrt.Scalar(Scalar a) -> Scalar',
-    'aten::sqrt(Tensor self) -> Tensor',
-    'aten::str(t elem) -> str',
-    'aten::sub(Scalar a, Scalar b) -> Scalar',
-    'aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor',
-    'aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor',
-    'aten::sub.complex(complex a, complex b) -> complex',
-    'aten::sub.complex_float(complex a, float b) -> complex',
-    'aten::sub.complex_int(complex a, int b) -> complex',
-    'aten::sub.float(float a, float b) -> float',
-    'aten::sub.float_complex(float a, complex b) -> complex',
-    'aten::sub.float_int(float a, int b) -> float',
-    'aten::sub.int(int a, int b) -> int',
-    'aten::sub.int_complex(int a, complex b) -> complex',
-    'aten::sub.int_float(int a, float b) -> float',
-    'aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)',
-    'aten::sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)',
-    'aten::sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)',
-    'aten::subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor',
-    'aten::subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor',
-    'aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)',
-    'aten::subtract_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)',
-    'aten::subtract_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)',
-    'aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor',
-    'aten::sum.DimnameList_out(Tensor self, str[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)',
-    'aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)',
-    'aten::sum.bool(bool[] self) -> int',
-    'aten::sum.complex(complex[] self) -> complex',
-    'aten::sum.dim_DimnameList(Tensor self, str[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor',
-    'aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor',
-    'aten::sum.float(float[] self) -> float',
-    'aten::sum.int(int[] self) -> int',
-    'aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)',
-    'aten::sum_to_size(Tensor self, SymInt[] size) -> Tensor',
-    'aten::tensor(t[] data, *, ScalarType? dtype=None, Device? device=None, bool requires_grad=False) -> Tensor',
-    'aten::tensor.bool(bool t, *, ScalarType? dtype=None, Device? device=None, bool requires_grad=False) -> Tensor',
-    'aten::tensor.complex(complex t, *, ScalarType? dtype=None, Device? device=None, bool requires_grad=False) -> Tensor',
-    'aten::tensor.float(float t, *, ScalarType? dtype=None, Device? device=None, bool requires_grad=False) -> Tensor',
-    'aten::tensor.int(int t, *, ScalarType? dtype=None, Device? device=None, bool requires_grad=False) -> Tensor',
-    'aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]',
-    'aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]',
-    'aten::tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[]',
-    'aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor',
-    'aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)',
-    'aten::values.bool(Dict(bool, t) self) -> t[](*)',
-    'aten::values.complex(Dict(complex, t) self) -> t[](*)',
-    'aten::values.float(Dict(float, t) self) -> t[](*)',
-    'aten::values.int(Dict(int, t) self) -> t[](*)',
-    'aten::values.str(Dict(str, t) self) -> t[](*)',
-    'aten::values.Tensor(Dict(Tensor, t) self) -> t[](*)',
-    'aten::values(Tensor(a) self) -> Tensor(a)',
-    'aten::warn(str message, int stacklevel=2) -> ()',
-    'prim::abs.complex(complex a) -> float',
-    'prim::abs.float(float a) -> float',
-    'prim::abs.int(int a) -> int',
-    'prim::abs.Scalar(Scalar a) -> Scalar',
-    'prim::abs(Tensor x) -> Tensor',
-    'prim::device(Tensor a) -> Device',
-    'prim::is_cpu(Tensor a) -> bool',
-    'prim::is_cuda(Tensor a) -> bool',
-    'prim::is_ipu(Tensor a) -> bool',
-    'prim::is_maia(Tensor a) -> bool',
-    'prim::is_meta(Tensor a) -> bool',
-    'prim::is_mkldnn(Tensor a) -> bool',
-    'prim::is_mps(Tensor a) -> bool',
-    'prim::is_mtia(Tensor a) -> bool',
-    'prim::is_nested(Tensor a) -> bool',
-    'prim::is_quantized(Tensor a) -> bool',
-    'prim::is_sparse_csr(Tensor a) -> bool',
-    'prim::is_sparse(Tensor a) -> bool',
-    'prim::is_vulkan(Tensor a) -> bool',
-    'prim::is_xla(Tensor a) -> bool',
-    'prim::is_xpu(Tensor a) -> bool',
-    'prim::itemsize(Tensor a) -> int',
-    'prim::layout(Tensor a) -> Layout',
-    'prim::max.bool_list(bool[] l, bool[] r) -> bool[]',
-    'prim::max.float_int(float a, int b) -> float',
-    'prim::max.float_list(float[] l, float[] r) -> float[]',
-    'prim::max.float(float a, float b) -> float',
-    'prim::max.int_float(int a, float b) -> float',
-    'prim::max.int_list(int[] l, int[] r) -> int[]',
-    'prim::max.int(int a, int b) -> int',
-    'prim::max.self_bool(bool[] self) -> bool',
-    'prim::max.self_float(float[] self) -> float',
-    'prim::max.self_int(int[] self) -> int',
-    'prim::max(Scalar a, Scalar b) -> Scalar',
-    'prim::min.bool_list(bool[] l, bool[] r) -> bool[]',
-    'prim::min.float_int(float a, int b) -> float',
-    'prim::min.float_list(float[] l, float[] r) -> float[]',
-    'prim::min.float(float a, float b) -> float',
-    'prim::min.int_float(int a, float b) -> float',
-    'prim::min.int_list(int[] l, int[] r) -> int[]',
-    'prim::min.int(int a, int b) -> int',
-    'prim::min.self_bool(bool[] self) -> bool',
-    'prim::min.self_float(float[] self) -> float',
-    'prim::min.self_int(int[] self) -> int',
-    'prim::min(Scalar a, Scalar b) -> Scalar',
-]
 
 known_legacy_schema_definitions = [
     '_caffe2::BBoxTransform(Tensor rois, Tensor deltas, Tensor im_info, float[] weights, bool apply_scale, bool rotated, bool angle_bound_on, int angle_bound_lo, int angle_bound_hi, float clip_angle_thresh, bool legacy_plus_one) -> (Tensor output_0, Tensor output_1)',
@@ -994,67 +101,40 @@ def _parse_schemas():
         content = '\n'.join(content)
         for value in entry[1].findall(content):
             value = re.sub(r'\n|\r|\s*"', '', value) if value.startswith('_caffe2::') else value
-            definition = entry[2] + value if len(entry) > 2 else value
-            if not definition in definitions:
-                definitions.add(definition)
-                schema = Schema(definition)
-                if schema.name in schemas:
-                    raise KeyError(schema.name)
-                schemas[schema.name] = schema
-    for value in known_legacy_schema_definitions:
-        schema = Schema(value)
-        schemas[schema.name] = schema
-    for value in known_schema_definitions:
-        schema = Schema(value)
-        schemas[schema.name] = schema
+            schema = entry[2] + value if len(entry) > 2 else value
+            if not schema in definitions:
+                definitions.add(schema)
+                key = schema.split('(', 1)[0].strip()
+                if key in schemas:
+                    raise KeyError(key)
+                schemas[key] = schema
+    for schema in known_legacy_schema_definitions:
+        key = schema.split('(', 1)[0].strip()
+        schemas[key] = schema
+    import torch # pylint: disable=import-outside-toplevel,import-error
+    all_schemas = list(torch._C._jit_get_all_schemas()) # pylint: disable=protected-access
+    for schema in all_schemas:
+        definition = str(schema)
+        key = definition.split('(', 1)[0].strip()
+        schemas[key] = definition
     return schemas
 
 def _filter_schemas(schemas, types):
-    keys = set(map(lambda _: _.split('.')[0], types.keys()))
+    names = set(map(lambda _: _.split('.')[0], types.keys()))
     filtered_schemas = set()
     for schema in schemas.values():
-        for key in keys:
-            if schema.name == key or schema.name.startswith(key + '.'):
-                filtered_schemas.add(schema.name)
-    # for schema in schemas.values():
-    #    if schema.name.startswith('aten::pop'):
-    #         filtered_schemas.add(schema.name)
-    # filtered_schemas = set(types.keys())
-    # content = _read('list.csv')
-    # regex = re.compile(r'Unsupported function \'(.*)\' in', re.MULTILINE)
-    # matches = set()
-    # for match in regex.findall(content):
-    #     if match.startswith('torch.'):
-    #         matches.add('aten::' + match[6:])
-    #     if match.startswith('ops.') and len(match.split('.')) > 2:
-    #         matches.add(match[4:].replace('.', '::'))
-    # for schema in schemas.values():
-    #     for match in matches:
-    #         if schema.name.startswith(match):
-    #             filtered_schemas.add(schema.name)
+        for name in names:
+            key = schema.split('(', 1)[0].strip()
+            if key == name or key.startswith(name + '.'):
+                filtered_schemas.add(key)
     return dict(filter(lambda _: _[0] in filtered_schemas, schemas.items()))
 
-def _check_schemas(schemas): # pylint: disable=unused-argument
-    # import torch
-    # for name in dir(torch.ops.aten):
-    #     if name.startswith('__') or name == 'name':
-    #         continue
-    #     packet = getattr(torch.ops.aten, name)
-    #     for overload in packet.overloads():
-    #         key = 'aten::' + name + ('.' + overload if overload != 'default' else '')
-    #         overload_schema = str(getattr(packet, overload)._schema)
-    #         if key in schemas:
-    #             schema = schemas[key]
-    #             if overload_schema != str(schema):
-    #                 print(overload_schema)
-    #                 print(schema)
-    pass
-
 def _check_types(types, schemas):
     types = dict(types.items())
     for schema in schemas.values():
-        if schema.name in types:
-            types.pop(schema.name)
+        key = schema.split('(', 1)[0].strip()
+        if key in types:
+            types.pop(key)
     for key in list(types.keys()):
         if key.startswith('torch.nn') or key.startswith('__torch__.'):
             types.pop(key)
@@ -1076,12 +156,13 @@ def _metadata():
     types = _read_metadata()
     schemas = _parse_schemas()
     _check_types(types, schemas)
-    _check_schemas(schemas)
     filtered_schemas = _filter_schemas(schemas, types)
-    metadata = Metadata(types)
     for schema in filtered_schemas.values():
-        value = metadata.type(schema)
-        value['name'] = schema.value
+        key = schema.split('(', 1)[0].strip()
+        if key in types:
+            types[key]['name'] = schema
+        else:
+            types[key] = { 'name': schema }
     _write_metadata(types)
 
 def main(): # pylint: disable=missing-function-docstring