CPred<"getElementTypeOrSelf($0).isa<mlir::quant::UniformQuantizedType>()">>;

// Checks if the given two values have the same type.
def AreTheSameElementType : Constraint<
  CPred<"$0.getType() == $1.getType()">>;

// Checks if the given two values are the same.
def AreTheSameValue : Constraint<
  CPred<"$0 == $1">>;

// Checks if the value has rank.
def HasRank : Constraint<

def IsRankedShapeLastDimStatic : Constraint<And<[
  CPred<"$0.getType().isa<RankedTensorType>()">,
  CPred<"!$0.getType().cast<ShapedType>().isDynamicDim( "
  "  $0.getType().cast<RankedTensorType>().getRank() - 1)">]>>;

def IsNotComplexType : Constraint<And<[
  CPred<"$0.getType().isa<RankedTensorType>()">,
  CPred<"!$0.getType().cast<ShapedType>().getElementType().isa<ComplexType>()">
]>>;

  NativeCodeCall<"TransposeLastTwoDims($0[0].getType())">;

def AreLastTwoDimsTransposed : Constraint<CPred<
  "TFL::AreLastTwoDimsTransposed($0)">>;

// Checks if the param passed is of NoneType.
def IsNoneType : Constraint<CPred<"$0.getType().isa<NoneType>()">>;

def ConstantLikePred : CPred<"::mlir::matchPattern($0, ::mlir::m_Constant())">;
def IsConstantLike : Constraint<ConstantLikePred>;

include "mhlo/IR/hlo_ops.td"
include "stablehlo/dialect/ChloOps.td"

def IsDefinedByConvOrDotGeneralOp : Constraint<Or<[
  CPred<"$0.getDefiningOp<mhlo::ConvolutionOp>()">,
  CPred<"$0.getDefiningOp<mhlo::DotGeneralOp>()">]>>;

def IsNull : Constraint<CPred<"!$0">>;

// This pattern optimizes:
//   conv/dot_general + a + b -> conv/dot_general + (a + b)
//   conv/dot_general - a - b -> conv/dot_general - (a + b)

// Here, the element type can be any integer or float type. But, note that only
// 32 bit integers are supported for the values.
class IsConstantValueOf<int value> : Constraint<CPred<
  "IsConstantValueOf($0," # value # ")">>;

def IsOnGpuDevicePred : CPred<"IsOnGpuDevice($0.getOwner())">;

  let description = [{
    The TFRT Fallback Dialect.
  }];

  let cppNamespace = "::tfrt::fallback";
}

// This corresponds to tensorflow::Tensor.
def TFTensorType : DialectType<Fallback_Dialect,
    CPred<"$_self.isa<::tfrt::fallback::TFTensorType>()">, "!tfrt_fallback.tf_tensor type">,
    BuildableType<"$_builder.getType<::tfrt::fallback::TFTensorType>()"> {
  let description = [{

def NoFallbackAttrNotSet : Constraint<CPred<
  "!$0.getDefiningOp()->hasAttr(kNoFallbackAttr)">>;

class FloatValueEquals<string val> : Constraint<CPred<
  "FloatValueEquals($0, " # val # ")">>;

class RankEquals<string rank> : Constraint<CPred<
  "RankEquals($0, " # rank # ")">>;

def IsFusibleWithBias : Constraint<CPred<
  "IsFusibleWithBiasOp($0.getDefiningOp())">>;

}

def MlrtFutureType : DialectType<Mlrt_Dialect,
    CPred<"$_self.isa<::mlrt::compiler::FutureType>()">, "!mlrt.future type">,
    BuildableType<"$_builder.getType<::mlrt::compiler::FutureType>()"> {
  let description = [{
    `!mlrt.future type` represents a C++ mlrt::Future.
  }];
}

def MlrtPromiseType : DialectType<Mlrt_Dialect,
    CPred<"$_self.isa<::mlrt::compiler::PromiseType>()">, "!mlrt.promise type">,

    Op<TensorflowMlrt_Dialect, mnemonic, !listconcat(traits, [IsolatedFromAbove])> {
}

// This corresponds to tensorflow::Tensor.
def TFTensorType : DialectType<TensorflowMlrt_Dialect,
    CPred<"$_self.isa<::tensorflow::tf_mlrt::TFTensorType>()">, "!tf_mlrt.tensor type">,
    BuildableType<"$_builder.getType<::tensorflow::tf_mlrt::TFTensorType>()"> {
  let description = [{

include "tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td"

// Only handles the case where precision config is default.
def IsPrecisionEmpty :
  Constraint<CPred<"IsPrecisionEmpty($0)">>;

// Creates Einsum Op from XlaDotV2 Op by generating equation.
def CreateEinsumOpFromXlaDotV2Op : NativeCodeCall<
  "CreateEinsumOpFromXlaDotV2Op($_builder, $_loc, $0...)">;