(TF_MaximumOp (TF_ConstOp:$cst $cst_val), $input),
  (TF_MaximumOp $input, $cst)>;

def SwapMinimumOperands : Pat<
  (TF_MinimumOp (TF_ConstOp:$cst $cst_val), $input),
  (TF_MinimumOp $input, $cst)>;

// Relu1 activation is represented as a couple of Max and Min ops, The following
// patterns recognize and keep them as TF ops so they can be converted to the
// TFLite Relu1 op.
def MatchRelu1Pattern1 : Pat<
  (TF_MinimumOp:$min_op

    // conversion, then dequantized during inference.
    // Activation: f32, Weight: qi8, Bias: f32
    WEIGHT_ONLY = 1;

    // Apply default dynamic range quantization. Quantized tensor value's
    // ranges are determined during graph runtime.
    // Activation: f32, Weight: qi8, Bias: f32
    POST_TRAINING_QUANTIZATION_DYNAMIC_RANGE = 2;

    there's any, and set it to True. The reason behind this decision is that
    generally activations of these ops show better accuracy with asymmetric
    input quantization so we want to deprecate symmetric activation quantization
    for those ops eventually.
    - Unlike to the old quantizer, per-channel quantization is supported for
    weight-only TransposeConvOp.
  }];

  let methods = [
    InterfaceMethod<

    // optimizations in the pipeline.
    METHOD_NO_QUANTIZE = 1;

    // Static range quantization. Quantized tensor values' ranges are statically
    // determined. The activation and weight are quantized to INT8 while bias is
    // quantized to INT32.
    METHOD_STATIC_RANGE_INT8 = 2;

    // Dynamic range quantization. Quantized tensor values' ranges are

  let cppNamespace = "::mlir::TFL";
}

def TFL_DimensionTypeAttr : EnumAttr<TFL_Dialect, TFL_DimensionType,
    "dimension_type_attr"> {
  let convertFromStorage = "$_self";
}

// Allowed activation function cases
// These should match the ActivationFunctionType enum in TFLite schema.
def TFL_AFEnum_None  : I32EnumAttrCase<"NONE", 0>;
def TFL_AFEnum_Relu  : I32EnumAttrCase<"RELU", 1>;