diff --git a/docs/reference/operators/MobileOps.md b/docs/reference/operators/MobileOps.md
index 54c5e4533a..697ea59280 100644
--- a/docs/reference/operators/MobileOps.md
+++ b/docs/reference/operators/MobileOps.md
@@ -10,6 +10,9 @@ These are the operators and types included in the ORT Mobile pre-built packages
 
 | Release | Documentation |
 |---------|---------------|
+| 1.14 | [Pre-Built Package Support](./mobile_package_op_type_support_1.14.md)|
+| 1.13 | [Pre-Built Package Support](./mobile_package_op_type_support_1.13.md)|
+| 1.12 | [Pre-Built Package Support](./mobile_package_op_type_support_1.12.md)|
 | 1.11 | [Pre-Built Package Support](./mobile_package_op_type_support_1.11.md)|
 | 1.10 | [Pre-Built Package Support](./mobile_package_op_type_support_1.10.md)|
 | 1.9 | [Pre-Built Package Support](./mobile_package_op_type_support_1.9.md)|
diff --git a/docs/reference/operators/mobile_package_op_type_support_1.12.md b/docs/reference/operators/mobile_package_op_type_support_1.12.md
new file mode 100644
index 0000000000..5cf3fd56ea
--- /dev/null
+++ b/docs/reference/operators/mobile_package_op_type_support_1.12.md
@@ -0,0 +1,139 @@
+---
+title: ORT 1.12 Mobile Package Operators
+parent: Operators
+grand_parent: Reference
+nav_exclude: true
+---
+
+# ONNX Runtime Mobile Pre-Built Package Operator and Type Support
+
+## Supported operators and types
+
+The supported operators and types are based on what is required to support float32 and quantized versions of popular models. The full list of input models used to determine this list is available [here](https://github.com/microsoft/onnxruntime/blob/master/tools/ci_build/github/android/mobile_package.required_operators.readme.txt)
+
+## Supported data input types
+
+  - float
+  - int8_t
+  - uint8_t
+
+NOTE: Operators used to manipulate dimensions and indices will support int32 and int64.
+
+## Supported Operators
+
+|Operator|Opsets|
+|--------|------|
+|**ai.onnx**||
+|ai.onnx:Abs|12, 13, 14, 15|
+|ai.onnx:Add|12, 13, 14, 15|
+|ai.onnx:And|12, 13, 14, 15|
+|ai.onnx:ArgMax|12, 13, 14, 15|
+|ai.onnx:ArgMin|12, 13, 14, 15|
+|ai.onnx:AveragePool|12, 13, 14, 15|
+|ai.onnx:Cast|12, 13, 14, 15|
+|ai.onnx:Ceil|12, 13, 14, 15|
+|ai.onnx:Clip|12, 13, 14, 15|
+|ai.onnx:Concat|12, 13, 14, 15|
+|ai.onnx:ConstantOfShape|12, 13, 14, 15|
+|ai.onnx:Conv|12, 13, 14, 15|
+|ai.onnx:ConvTranspose|12, 13, 14, 15|
+|ai.onnx:Cos|12, 13, 14, 15|
+|ai.onnx:CumSum|12, 13, 14, 15|
+|ai.onnx:DepthToSpace|12, 13, 14, 15|
+|ai.onnx:DequantizeLinear|12, 13, 14, 15|
+|ai.onnx:Div|12, 13, 14, 15|
+|ai.onnx:DynamicQuantizeLinear|12, 13, 14, 15|
+|ai.onnx:Elu|12, 13, 14, 15|
+|ai.onnx:Equal|12, 13, 14, 15|
+|ai.onnx:Erf|12, 13, 14, 15|
+|ai.onnx:Exp|12, 13, 14, 15|
+|ai.onnx:Expand|12, 13, 14, 15|
+|ai.onnx:Flatten|12, 13, 14, 15|
+|ai.onnx:Floor|12, 13, 14, 15|
+|ai.onnx:Gather|12, 13, 14, 15|
+|ai.onnx:GatherND|12, 13, 14, 15|
+|ai.onnx:Gemm|12, 13, 14, 15|
+|ai.onnx:GlobalAveragePool|12, 13, 14, 15|
+|ai.onnx:Greater|12, 13, 14, 15|
+|ai.onnx:GreaterOrEqual|12, 13, 14, 15|
+|ai.onnx:HardSigmoid|12, 13, 14, 15|
+|ai.onnx:Identity|12, 13, 14, 15|
+|ai.onnx:If|12, 13, 14, 15|
+|ai.onnx:InstanceNormalization|12, 13, 14, 15|
+|ai.onnx:LRN|12, 13, 14, 15|
+|ai.onnx:LayerNormalization|1|
+|ai.onnx:LeakyRelu|12, 13, 14, 15|
+|ai.onnx:Less|12, 13, 14, 15|
+|ai.onnx:LessOrEqual|12, 13, 14, 15|
+|ai.onnx:Log|12, 13, 14, 15|
+|ai.onnx:LogSoftmax|12, 13, 14, 15|
+|ai.onnx:Loop|12, 13, 14, 15|
+|ai.onnx:MatMul|12, 13, 14, 15|
+|ai.onnx:MatMulInteger|12, 13, 14, 15|
+|ai.onnx:Max|12, 13, 14, 15|
+|ai.onnx:MaxPool|12, 13, 14, 15|
+|ai.onnx:Mean|12, 13, 14, 15|
+|ai.onnx:Min|12, 13, 14, 15|
+|ai.onnx:Mul|12, 13, 14, 15|
+|ai.onnx:Neg|12, 13, 14, 15|
+|ai.onnx:NonMaxSuppression|12, 13, 14, 15|
+|ai.onnx:NonZero|12, 13, 14, 15|
+|ai.onnx:Not|12, 13, 14, 15|
+|ai.onnx:Or|12, 13, 14, 15|
+|ai.onnx:PRelu|12, 13, 14, 15|
+|ai.onnx:Pad|12, 13, 14, 15|
+|ai.onnx:Pow|12, 13, 14, 15|
+|ai.onnx:QLinearConv|12, 13, 14, 15|
+|ai.onnx:QLinearMatMul|12, 13, 14, 15|
+|ai.onnx:QuantizeLinear|12, 13, 14, 15|
+|ai.onnx:Range|12, 13, 14, 15|
+|ai.onnx:Reciprocal|12, 13, 14, 15|
+|ai.onnx:ReduceMax|12, 13, 14, 15|
+|ai.onnx:ReduceMean|12, 13, 14, 15|
+|ai.onnx:ReduceMin|12, 13, 14, 15|
+|ai.onnx:ReduceProd|12, 13, 14, 15|
+|ai.onnx:ReduceSum|12, 13, 14, 15|
+|ai.onnx:Relu|12, 13, 14, 15|
+|ai.onnx:Reshape|12, 13, 14, 15|
+|ai.onnx:Resize|12, 13, 14, 15|
+|ai.onnx:ReverseSequence|12, 13, 14, 15|
+|ai.onnx:Round|12, 13, 14, 15|
+|ai.onnx:Scan|12, 13, 14, 15|
+|ai.onnx:ScatterND|12, 13, 14, 15|
+|ai.onnx:Shape|12, 13, 14, 15|
+|ai.onnx:Sigmoid|12, 13, 14, 15|
+|ai.onnx:Sin|12, 13, 14, 15|
+|ai.onnx:Size|12, 13, 14, 15|
+|ai.onnx:Slice|12, 13, 14, 15|
+|ai.onnx:Softmax|12, 13, 14, 15|
+|ai.onnx:SpaceToDepth|12, 13, 14, 15|
+|ai.onnx:Split|12, 13, 14, 15|
+|ai.onnx:Sqrt|12, 13, 14, 15|
+|ai.onnx:Squeeze|12, 13, 14, 15|
+|ai.onnx:Sub|12, 13, 14, 15|
+|ai.onnx:Sum|12, 13, 14, 15|
+|ai.onnx:Tanh|12, 13, 14, 15|
+|ai.onnx:ThresholdedRelu|12, 13, 14, 15|
+|ai.onnx:Tile|12, 13, 14, 15|
+|ai.onnx:TopK|12, 13, 14, 15|
+|ai.onnx:Transpose|12, 13, 14, 15|
+|ai.onnx:Unique|12, 13, 14, 15|
+|ai.onnx:Unsqueeze|12, 13, 14, 15|
+|ai.onnx:Where|12, 13, 14, 15|
+|||
+|**com.microsoft**||
+|com.microsoft:DynamicQuantizeMatMul|1|
+|com.microsoft:FusedConv|1|
+|com.microsoft:FusedGemm|1|
+|com.microsoft:FusedMatMul|1|
+|com.microsoft:Gelu|1|
+|com.microsoft:MatMulIntegerToFloat|1|
+|com.microsoft:NhwcMaxPool|1|
+|com.microsoft:QLinearAdd|1|
+|com.microsoft:QLinearAveragePool|1|
+|com.microsoft:QLinearConv|1|
+|com.microsoft:QLinearGlobalAveragePool|1|
+|com.microsoft:QLinearLeakyRelu|1|
+|com.microsoft:QLinearMul|1|
+|com.microsoft:QLinearSigmoid|1|
+|||
diff --git a/docs/reference/operators/mobile_package_op_type_support_1.13.md b/docs/reference/operators/mobile_package_op_type_support_1.13.md
new file mode 100644
index 0000000000..b6035ab058
--- /dev/null
+++ b/docs/reference/operators/mobile_package_op_type_support_1.13.md
@@ -0,0 +1,139 @@
+---
+title: ORT 1.13 Mobile Package Operators
+parent: Operators
+grand_parent: Reference
+nav_exclude: true
+---
+
+# ONNX Runtime Mobile Pre-Built Package Operator and Type Support
+
+## Supported operators and types
+
+The supported operators and types are based on what is required to support float32 and quantized versions of popular models. The full list of input models used to determine this list is available [here](https://github.com/microsoft/onnxruntime/blob/main/tools/ci_build/github/android/mobile_package.required_operators.readme.txt)
+
+## Supported data input types
+
+  - float
+  - int8_t
+  - uint8_t
+
+NOTE: Operators used to manipulate dimensions and indices will support int32 and int64.
+
+## Supported Operators
+
+|Operator|Opsets|
+|--------|------|
+|**ai.onnx**||
+|ai.onnx:Abs|12, 13, 14, 15|
+|ai.onnx:Add|12, 13, 14, 15|
+|ai.onnx:And|12, 13, 14, 15|
+|ai.onnx:ArgMax|12, 13, 14, 15|
+|ai.onnx:ArgMin|12, 13, 14, 15|
+|ai.onnx:AveragePool|12, 13, 14, 15|
+|ai.onnx:Cast|12, 13, 14, 15|
+|ai.onnx:Ceil|12, 13, 14, 15|
+|ai.onnx:Clip|12, 13, 14, 15|
+|ai.onnx:Concat|12, 13, 14, 15|
+|ai.onnx:ConstantOfShape|12, 13, 14, 15|
+|ai.onnx:Conv|12, 13, 14, 15|
+|ai.onnx:ConvTranspose|12, 13, 14, 15|
+|ai.onnx:Cos|12, 13, 14, 15|
+|ai.onnx:CumSum|12, 13, 14, 15|
+|ai.onnx:DepthToSpace|12, 13, 14, 15|
+|ai.onnx:DequantizeLinear|12, 13, 14, 15|
+|ai.onnx:Div|12, 13, 14, 15|
+|ai.onnx:DynamicQuantizeLinear|12, 13, 14, 15|
+|ai.onnx:Elu|12, 13, 14, 15|
+|ai.onnx:Equal|12, 13, 14, 15|
+|ai.onnx:Erf|12, 13, 14, 15|
+|ai.onnx:Exp|12, 13, 14, 15|
+|ai.onnx:Expand|12, 13, 14, 15|
+|ai.onnx:Flatten|12, 13, 14, 15|
+|ai.onnx:Floor|12, 13, 14, 15|
+|ai.onnx:Gather|12, 13, 14, 15|
+|ai.onnx:GatherND|12, 13, 14, 15|
+|ai.onnx:Gemm|12, 13, 14, 15|
+|ai.onnx:GlobalAveragePool|12, 13, 14, 15|
+|ai.onnx:Greater|12, 13, 14, 15|
+|ai.onnx:GreaterOrEqual|12, 13, 14, 15|
+|ai.onnx:HardSigmoid|12, 13, 14, 15|
+|ai.onnx:Identity|12, 13, 14, 15|
+|ai.onnx:If|12, 13, 14, 15|
+|ai.onnx:InstanceNormalization|12, 13, 14, 15|
+|ai.onnx:LRN|12, 13, 14, 15|
+|ai.onnx:LayerNormalization|1|
+|ai.onnx:LeakyRelu|12, 13, 14, 15|
+|ai.onnx:Less|12, 13, 14, 15|
+|ai.onnx:LessOrEqual|12, 13, 14, 15|
+|ai.onnx:Log|12, 13, 14, 15|
+|ai.onnx:LogSoftmax|12, 13, 14, 15|
+|ai.onnx:Loop|12, 13, 14, 15|
+|ai.onnx:MatMul|12, 13, 14, 15|
+|ai.onnx:MatMulInteger|12, 13, 14, 15|
+|ai.onnx:Max|12, 13, 14, 15|
+|ai.onnx:MaxPool|12, 13, 14, 15|
+|ai.onnx:Mean|12, 13, 14, 15|
+|ai.onnx:Min|12, 13, 14, 15|
+|ai.onnx:Mul|12, 13, 14, 15|
+|ai.onnx:Neg|12, 13, 14, 15|
+|ai.onnx:NonMaxSuppression|12, 13, 14, 15|
+|ai.onnx:NonZero|12, 13, 14, 15|
+|ai.onnx:Not|12, 13, 14, 15|
+|ai.onnx:Or|12, 13, 14, 15|
+|ai.onnx:PRelu|12, 13, 14, 15|
+|ai.onnx:Pad|12, 13, 14, 15|
+|ai.onnx:Pow|12, 13, 14, 15|
+|ai.onnx:QLinearConv|12, 13, 14, 15|
+|ai.onnx:QLinearMatMul|12, 13, 14, 15|
+|ai.onnx:QuantizeLinear|12, 13, 14, 15|
+|ai.onnx:Range|12, 13, 14, 15|
+|ai.onnx:Reciprocal|12, 13, 14, 15|
+|ai.onnx:ReduceMax|12, 13, 14, 15|
+|ai.onnx:ReduceMean|12, 13, 14, 15|
+|ai.onnx:ReduceMin|12, 13, 14, 15|
+|ai.onnx:ReduceProd|12, 13, 14, 15|
+|ai.onnx:ReduceSum|12, 13, 14, 15|
+|ai.onnx:Relu|12, 13, 14, 15|
+|ai.onnx:Reshape|12, 13, 14, 15|
+|ai.onnx:Resize|12, 13, 14, 15|
+|ai.onnx:ReverseSequence|12, 13, 14, 15|
+|ai.onnx:Round|12, 13, 14, 15|
+|ai.onnx:Scan|12, 13, 14, 15|
+|ai.onnx:ScatterND|12, 13, 14, 15|
+|ai.onnx:Shape|12, 13, 14, 15|
+|ai.onnx:Sigmoid|12, 13, 14, 15|
+|ai.onnx:Sin|12, 13, 14, 15|
+|ai.onnx:Size|12, 13, 14, 15|
+|ai.onnx:Slice|12, 13, 14, 15|
+|ai.onnx:Softmax|12, 13, 14, 15|
+|ai.onnx:SpaceToDepth|12, 13, 14, 15|
+|ai.onnx:Split|12, 13, 14, 15|
+|ai.onnx:Sqrt|12, 13, 14, 15|
+|ai.onnx:Squeeze|12, 13, 14, 15|
+|ai.onnx:Sub|12, 13, 14, 15|
+|ai.onnx:Sum|12, 13, 14, 15|
+|ai.onnx:Tanh|12, 13, 14, 15|
+|ai.onnx:ThresholdedRelu|12, 13, 14, 15|
+|ai.onnx:Tile|12, 13, 14, 15|
+|ai.onnx:TopK|12, 13, 14, 15|
+|ai.onnx:Transpose|12, 13, 14, 15|
+|ai.onnx:Unique|12, 13, 14, 15|
+|ai.onnx:Unsqueeze|12, 13, 14, 15|
+|ai.onnx:Where|12, 13, 14, 15|
+|||
+|**com.microsoft**||
+|com.microsoft:DynamicQuantizeMatMul|1|
+|com.microsoft:FusedConv|1|
+|com.microsoft:FusedGemm|1|
+|com.microsoft:FusedMatMul|1|
+|com.microsoft:Gelu|1|
+|com.microsoft:MatMulIntegerToFloat|1|
+|com.microsoft:NhwcMaxPool|1|
+|com.microsoft:QLinearAdd|1|
+|com.microsoft:QLinearAveragePool|1|
+|com.microsoft:QLinearConv|1|
+|com.microsoft:QLinearGlobalAveragePool|1|
+|com.microsoft:QLinearLeakyRelu|1|
+|com.microsoft:QLinearMul|1|
+|com.microsoft:QLinearSigmoid|1|
+|||
diff --git a/docs/reference/operators/mobile_package_op_type_support_1.14.md b/docs/reference/operators/mobile_package_op_type_support_1.14.md
new file mode 100644
index 0000000000..9560487112
--- /dev/null
+++ b/docs/reference/operators/mobile_package_op_type_support_1.14.md
@@ -0,0 +1,139 @@
+---
+title: ORT 1.14 Mobile Package Operators
+parent: Operators
+grand_parent: Reference
+nav_exclude: true
+---
+
+# ONNX Runtime Mobile Pre-Built Package Operator and Type Support
+
+## Supported operators and types
+
+The supported operators and types are based on what is required to support float32 and quantized versions of popular models. The full list of input models used to determine this list is available [here](https://github.com/microsoft/onnxruntime/blob/main/tools/ci_build/github/android/mobile_package.required_operators.readme.txt)
+
+## Supported data input types
+
+  - float
+  - int8_t
+  - uint8_t
+
+NOTE: Operators used to manipulate dimensions and indices will support int32 and int64.
+
+## Supported Operators
+
+|Operator|Opsets|
+|--------|------|
+|**ai.onnx**||
+|ai.onnx:Abs|12, 13, 14, 15|
+|ai.onnx:Add|12, 13, 14, 15|
+|ai.onnx:And|12, 13, 14, 15|
+|ai.onnx:ArgMax|12, 13, 14, 15|
+|ai.onnx:ArgMin|12, 13, 14, 15|
+|ai.onnx:AveragePool|12, 13, 14, 15|
+|ai.onnx:Cast|12, 13, 14, 15|
+|ai.onnx:Ceil|12, 13, 14, 15|
+|ai.onnx:Clip|12, 13, 14, 15|
+|ai.onnx:Concat|12, 13, 14, 15|
+|ai.onnx:ConstantOfShape|12, 13, 14, 15|
+|ai.onnx:Conv|12, 13, 14, 15|
+|ai.onnx:ConvTranspose|12, 13, 14, 15|
+|ai.onnx:Cos|12, 13, 14, 15|
+|ai.onnx:CumSum|12, 13, 14, 15|
+|ai.onnx:DepthToSpace|12, 13, 14, 15|
+|ai.onnx:DequantizeLinear|12, 13, 14, 15|
+|ai.onnx:Div|12, 13, 14, 15|
+|ai.onnx:DynamicQuantizeLinear|12, 13, 14, 15|
+|ai.onnx:Elu|12, 13, 14, 15|
+|ai.onnx:Equal|12, 13, 14, 15|
+|ai.onnx:Erf|12, 13, 14, 15|
+|ai.onnx:Exp|12, 13, 14, 15|
+|ai.onnx:Expand|12, 13, 14, 15|
+|ai.onnx:Flatten|12, 13, 14, 15|
+|ai.onnx:Floor|12, 13, 14, 15|
+|ai.onnx:Gather|12, 13, 14, 15|
+|ai.onnx:GatherND|12, 13, 14, 15|
+|ai.onnx:Gemm|12, 13, 14, 15|
+|ai.onnx:GlobalAveragePool|12, 13, 14, 15|
+|ai.onnx:Greater|12, 13, 14, 15|
+|ai.onnx:GreaterOrEqual|12, 13, 14, 15|
+|ai.onnx:HardSigmoid|12, 13, 14, 15|
+|ai.onnx:Identity|12, 13, 14, 15|
+|ai.onnx:If|12, 13, 14, 15|
+|ai.onnx:InstanceNormalization|12, 13, 14, 15|
+|ai.onnx:LRN|12, 13, 14, 15|
+|ai.onnx:LayerNormalization|1|
+|ai.onnx:LeakyRelu|12, 13, 14, 15|
+|ai.onnx:Less|12, 13, 14, 15|
+|ai.onnx:LessOrEqual|12, 13, 14, 15|
+|ai.onnx:Log|12, 13, 14, 15|
+|ai.onnx:LogSoftmax|12, 13, 14, 15|
+|ai.onnx:Loop|12, 13, 14, 15|
+|ai.onnx:MatMul|12, 13, 14, 15|
+|ai.onnx:MatMulInteger|12, 13, 14, 15|
+|ai.onnx:Max|12, 13, 14, 15|
+|ai.onnx:MaxPool|12, 13, 14, 15|
+|ai.onnx:Mean|12, 13, 14, 15|
+|ai.onnx:Min|12, 13, 14, 15|
+|ai.onnx:Mul|12, 13, 14, 15|
+|ai.onnx:Neg|12, 13, 14, 15|
+|ai.onnx:NonMaxSuppression|12, 13, 14, 15|
+|ai.onnx:NonZero|12, 13, 14, 15|
+|ai.onnx:Not|12, 13, 14, 15|
+|ai.onnx:Or|12, 13, 14, 15|
+|ai.onnx:PRelu|12, 13, 14, 15|
+|ai.onnx:Pad|12, 13, 14, 15|
+|ai.onnx:Pow|12, 13, 14, 15|
+|ai.onnx:QLinearConv|12, 13, 14, 15|
+|ai.onnx:QLinearMatMul|12, 13, 14, 15|
+|ai.onnx:QuantizeLinear|12, 13, 14, 15|
+|ai.onnx:Range|12, 13, 14, 15|
+|ai.onnx:Reciprocal|12, 13, 14, 15|
+|ai.onnx:ReduceMax|12, 13, 14, 15|
+|ai.onnx:ReduceMean|12, 13, 14, 15|
+|ai.onnx:ReduceMin|12, 13, 14, 15|
+|ai.onnx:ReduceProd|12, 13, 14, 15|
+|ai.onnx:ReduceSum|12, 13, 14, 15|
+|ai.onnx:Relu|12, 13, 14, 15|
+|ai.onnx:Reshape|12, 13, 14, 15|
+|ai.onnx:Resize|12, 13, 14, 15|
+|ai.onnx:ReverseSequence|12, 13, 14, 15|
+|ai.onnx:Round|12, 13, 14, 15|
+|ai.onnx:Scan|12, 13, 14, 15|
+|ai.onnx:ScatterND|12, 13, 14, 15|
+|ai.onnx:Shape|12, 13, 14, 15|
+|ai.onnx:Sigmoid|12, 13, 14, 15|
+|ai.onnx:Sin|12, 13, 14, 15|
+|ai.onnx:Size|12, 13, 14, 15|
+|ai.onnx:Slice|12, 13, 14, 15|
+|ai.onnx:Softmax|12, 13, 14, 15|
+|ai.onnx:SpaceToDepth|12, 13, 14, 15|
+|ai.onnx:Split|12, 13, 14, 15|
+|ai.onnx:Sqrt|12, 13, 14, 15|
+|ai.onnx:Squeeze|12, 13, 14, 15|
+|ai.onnx:Sub|12, 13, 14, 15|
+|ai.onnx:Sum|12, 13, 14, 15|
+|ai.onnx:Tanh|12, 13, 14, 15|
+|ai.onnx:ThresholdedRelu|12, 13, 14, 15|
+|ai.onnx:Tile|12, 13, 14, 15|
+|ai.onnx:TopK|12, 13, 14, 15|
+|ai.onnx:Transpose|12, 13, 14, 15|
+|ai.onnx:Unique|12, 13, 14, 15|
+|ai.onnx:Unsqueeze|12, 13, 14, 15|
+|ai.onnx:Where|12, 13, 14, 15|
+|||
+|**com.microsoft**||
+|com.microsoft:DynamicQuantizeMatMul|1|
+|com.microsoft:FusedConv|1|
+|com.microsoft:FusedGemm|1|
+|com.microsoft:FusedMatMul|1|
+|com.microsoft:Gelu|1|
+|com.microsoft:MatMulIntegerToFloat|1|
+|com.microsoft:NhwcMaxPool|1|
+|com.microsoft:QLinearAdd|1|
+|com.microsoft:QLinearAveragePool|1|
+|com.microsoft:QLinearConv|1|
+|com.microsoft:QLinearGlobalAveragePool|1|
+|com.microsoft:QLinearLeakyRelu|1|
+|com.microsoft:QLinearMul|1|
+|com.microsoft:QLinearSigmoid|1|
+|||