//===-------------------------------------------------------*- tablegen -*-===//
//
// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
// Operation summaries and descriptions were systematically derived from public
// API docstrings and are licensed accordingly:
//   https://github.com/pytorch/pytorch/blob/master/LICENSE
//===----------------------------------------------------------------------===//
//
// This file is automatically generated.  Please do not edit.
// Generated via:
//   python -m torch_mlir_utils.codegen.torch_ods_gen
//
//===----------------------------------------------------------------------===//

def Torch_AtenTanhOp : Torch_Op<"aten.tanh", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::tanh : (Tensor) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
}

def Torch_AtenTanh_Op : Torch_Op<"aten.tanh_", [
    IsTrailingUnderscoreInplaceVariant,
    AllowsTypeRefinement
  ]> {
  let summary = "Generated op for `aten::tanh_ : (Tensor) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
}

def Torch_AtenReluOp : Torch_Op<"aten.relu", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::relu : (Tensor) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
}

def Torch_AtenRelu_Op : Torch_Op<"aten.relu_", [
    IsTrailingUnderscoreInplaceVariant,
    AllowsTypeRefinement
  ]> {
  let summary = "Generated op for `aten::relu_ : (Tensor) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
}

def Torch_AtenAddTensorOp : Torch_Op<"aten.add.Tensor", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::add.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self,
    AnyTorchTensorType:$other,
    AnyTorchScalarType:$alpha
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self `,` $other `,` $alpha attr-dict `:` type($self) `,` type($other) `,` type($alpha) `->` type($result)";
}

def Torch_AtenAdd_TensorOp : Torch_Op<"aten.add_.Tensor", [
    IsTrailingUnderscoreInplaceVariant,
    AllowsTypeRefinement
  ]> {
  let summary = "Generated op for `aten::add_.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self,
    AnyTorchTensorType:$other,
    AnyTorchScalarType:$alpha
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self `,` $other `,` $alpha attr-dict `:` type($self) `,` type($other) `,` type($alpha) `->` type($result)";
}

def Torch_AtenLinearOp : Torch_Op<"aten.linear", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::linear : (Tensor, Tensor, Tensor?) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$input,
    AnyTorchTensorType:$weight,
    AnyTorchOptionalTensor:$bias
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$input `,` $weight `,` $bias attr-dict `:` type($input) `,` type($weight) `,` type($bias) `->` type($result)";
}

def Torch_AtenMmOp : Torch_Op<"aten.mm", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::mm : (Tensor, Tensor) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self,
    AnyTorchTensorType:$mat2
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self `,` $mat2 attr-dict `:` type($self) `,` type($mat2) `->` type($result)";
}

def Torch_AtenConv2dOp : Torch_Op<"aten.conv2d", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::conv2d : (Tensor, Tensor, Tensor?, int[], int[], int[], int) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$input,
    AnyTorchTensorType:$weight,
    AnyTorchOptionalTensor:$bias,
    AnyTorchIntListType:$stride,
    AnyTorchIntListType:$padding,
    AnyTorchIntListType:$dilation,
    AnyTorchIntType:$groups
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$input `,` $weight `,` $bias `,` $stride `,` $padding `,` $dilation `,` $groups attr-dict `:` type($input) `,` type($weight) `,` type($bias) `,` type($stride) `,` type($padding) `,` type($dilation) `,` type($groups) `->` type($result)";
}

def Torch_AtenBatchNormOp : Torch_Op<"aten.batch_norm", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::batch_norm : (Tensor, Tensor?, Tensor?, Tensor?, Tensor?, bool, float, float, bool) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$input,
    AnyTorchOptionalTensor:$weight,
    AnyTorchOptionalTensor:$bias,
    AnyTorchOptionalTensor:$running_mean,
    AnyTorchOptionalTensor:$running_var,
    AnyTorchBoolType:$training,
    AnyFloat:$momentum,
    AnyFloat:$eps,
    AnyTorchBoolType:$cudnn_enabled
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$input `,` $weight `,` $bias `,` $running_mean `,` $running_var `,` $training `,` $momentum `,` $eps `,` $cudnn_enabled attr-dict `:` type($input) `,` type($weight) `,` type($bias) `,` type($running_mean) `,` type($running_var) `,` type($training) `,` type($momentum) `,` type($eps) `,` type($cudnn_enabled) `->` type($result)";
}

def Torch_AtenMaxPool2dOp : Torch_Op<"aten.max_pool2d", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::max_pool2d : (Tensor, int[], int[], int[], int[], bool) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self,
    AnyTorchIntListType:$kernel_size,
    AnyTorchIntListType:$stride,
    AnyTorchIntListType:$padding,
    AnyTorchIntListType:$dilation,
    AnyTorchBoolType:$ceil_mode
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self `,` $kernel_size `,` $stride `,` $padding `,` $dilation `,` $ceil_mode attr-dict `:` type($self) `,` type($kernel_size) `,` type($stride) `,` type($padding) `,` type($dilation) `,` type($ceil_mode) `->` type($result)";
}

def Torch_AtenAdaptiveAvgPool2dOp : Torch_Op<"aten.adaptive_avg_pool2d", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::adaptive_avg_pool2d : (Tensor, int[]) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self,
    AnyTorchIntListType:$output_size
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self `,` $output_size attr-dict `:` type($self) `,` type($output_size) `->` type($result)";
}

def Torch_AtenFlattenUsingIntsOp : Torch_Op<"aten.flatten.using_ints", [
    AllowsTypeRefinement
  ]> {
  let summary = "Generated op for `aten::flatten.using_ints : (Tensor, int, int) -> (Tensor)`";
  let arguments = (ins
    AnyTorchTensorType:$self,
    AnyTorchIntType:$start_dim,
    AnyTorchIntType:$end_dim
  );
  let results = (outs
    AnyTorchTensorType:$result
  );
  let assemblyFormat = "$self `,` $start_dim `,` $end_dim attr-dict `:` type($self) `,` type($start_dim) `,` type($end_dim) `->` type($result)";
}

def Torch_AtenDimOp : Torch_Op<"aten.dim", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::dim : (Tensor) -> (int)`";
  let arguments = (ins
    AnyTorchTensorType:$self
  );
  let results = (outs
    AnyTorchIntType:$result
  );
  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
}

def Torch_AtenSizeOp : Torch_Op<"aten.size", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::size : (Tensor) -> (int[])`";
  let arguments = (ins
    AnyTorchTensorType:$self
  );
  let results = (outs
    AnyTorchIntListType:$result
  );
  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
  let hasCanonicalizer = 1;
}

def Torch_AtenGtIntOp : Torch_Op<"aten.gt.int", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::gt.int : (int, int) -> (bool)`";
  let arguments = (ins
    AnyTorchIntType:$a,
    AnyTorchIntType:$b
  );
  let results = (outs
    AnyTorchBoolType:$result
  );
  let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
}

def Torch_AtenNeIntOp : Torch_Op<"aten.ne.int", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::ne.int : (int, int) -> (bool)`";
  let arguments = (ins
    AnyTorchIntType:$a,
    AnyTorchIntType:$b
  );
  let results = (outs
    AnyTorchBoolType:$result
  );
  let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
}

def Torch_AtenAddIntOp : Torch_Op<"aten.add.int", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::add.int : (int, int) -> (int)`";
  let arguments = (ins
    AnyTorchIntType:$a,
    AnyTorchIntType:$b
  );
  let results = (outs
    AnyTorchIntType:$result
  );
  let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
}

def Torch_AtenMulIntOp : Torch_Op<"aten.mul.int", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::mul.int : (int, int) -> (int)`";
  let arguments = (ins
    AnyTorchIntType:$a,
    AnyTorchIntType:$b
  );
  let results = (outs
    AnyTorchIntType:$result
  );
  let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
}

def Torch_AtenAddFloatIntOp : Torch_Op<"aten.add.float_int", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::add.float_int : (float, int) -> (float)`";
  let arguments = (ins
    AnyFloat:$a,
    AnyTorchIntType:$b
  );
  let results = (outs
    AnyFloat:$result
  );
  let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
}

def Torch_AtenMulFloatOp : Torch_Op<"aten.mul.float", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::mul.float : (float, float) -> (float)`";
  let arguments = (ins
    AnyFloat:$a,
    AnyFloat:$b
  );
  let results = (outs
    AnyFloat:$result
  );
  let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
}

def Torch_AtenLtFloatIntOp : Torch_Op<"aten.lt.float_int", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::lt.float_int : (float, int) -> (bool)`";
  let arguments = (ins
    AnyFloat:$a,
    AnyTorchIntType:$b
  );
  let results = (outs
    AnyTorchBoolType:$result
  );
  let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
}

def Torch_Aten__Is__Op : Torch_Op<"aten.__is__", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::__is__ : (t1, t2) -> (bool)`";
  let arguments = (ins
    AnyTorchType:$self,
    AnyTorchType:$obj
  );
  let results = (outs
    AnyTorchBoolType:$result
  );
  let assemblyFormat = "$self `,` $obj attr-dict `:` type($self) `,` type($obj) `->` type($result)";
  let hasFolder = 1;
}

def Torch_AtenLenTOp : Torch_Op<"aten.len.t", [
    AllowsTypeRefinement,
    HasValueSemantics
  ]> {
  let summary = "Generated op for `aten::len.t : (t[]) -> (int)`";
  let arguments = (ins
    Basicpy_ListType:$a
  );
  let results = (outs
    AnyTorchIntType:$result
  );
  let assemblyFormat = "$a attr-dict `:` type($a) `->` type($result)";
  let hasCanonicalizer = 1;
}