using OpRewritePattern<TFL::PackOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(TFL::PackOp pack_op,
                                PatternRewriter& rewriter) const override;
};

struct SquaredDifference : public OpRewritePattern<TFL::SquaredDifferenceOp> {
  using OpRewritePattern<TFL::SquaredDifferenceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(TFL::SquaredDifferenceOp squared_diff_op,

 private:
  LogicalResult CreateNMSCustomOptions(func::FuncOp func, DictionaryAttr attrs,
                                       std::string& custom_option_buffer);

  LogicalResult AddIntAttr(func::FuncOp func, DictionaryAttr attrs,
                           const std::string& attribute,
                           flexbuffers::Builder* builder);

  LogicalResult AddFloatAttr(func::FuncOp func, DictionaryAttr attrs,

      : RewritePattern(MatchAnyOpTypeTag(), 1, context) {}

  LogicalResult matchAndRewrite(Operation* op,
                                PatternRewriter& rewriter) const override;

 private:
  template <typename Op>
  LogicalResult RewriteEqOp(Operation* op, PatternRewriter& rewriter) const;

  LogicalResult RewriteOp(
      Operation* op, PatternRewriter& rewriter,

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_op_interfaces.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"

namespace mlir {
namespace OpTrait {
namespace TF {

// Verifies if 'ref_type' is a REF type corresponding to 'type'.
static inline LogicalResult VerifyRefTypeMatch(mlir::Type type,

namespace mlir::quant {

LogicalResult FillAttributesForUniformQuantizedDotOp(
    PatternRewriter& rewriter, Operation* op,
    llvm::StringMap<Attribute>& identifier_to_attr,
    tensorflow::quantization::QuantizationMethod::PresetMethod
        quantization_method,
    bool enable_per_channel_quantization);

LogicalResult FillAttributesForUniformQuantizedConvolutionOp(

 public:
  explicit ConvertMaxUnpoolingFunc(func::FuncOp func, mlir::TF::FuncAttr attr)
      : func_(func), attr_(attr) {}

  LogicalResult RewriteFunc();

  LogicalResult VerifySignature();

 private:
  LogicalResult CreateCustomOptions(std::string& custom_option_buffer);

  func::FuncOp func_;
  mlir::TF::FuncAttr attr_;
};

// Fuse DenseImageWarp ops annotated by tf.function to a TFLite custom op.

// or human readable.
mlir::LogicalResult DecodeShardingAttribute(const std::string& shard_str,
                                            xla::OpSharding& sharding,
                                            bool report_error = true);

// Encodes the sharding in human readable form.
mlir::LogicalResult DecodeShardingAttribute(mlir::Attribute shard_attr,

  // Emits an error on failure.
  mlir::LogicalResult PrepareParams();

  // Given the required_consts, it will fill the 3 output vectors with
  // their respective data.
  // Expressions: Output XLA expressions as required by the compiled kernel.
  // Tensors: Vector of tensors that back the TensorValue inputs
  // Inputs: Vector of inputs that are backed by tensors.
  mlir::LogicalResult PrepareKernelInputs(

               << rank << "); actual value: " << axis;
      }
    }
  }

  return success();
}
LogicalResult CumprodOp::verify() { return Verify(*this); }
LogicalResult CumsumOp::verify() { return Verify(*this); }
LogicalResult CumulativeLogsumexpOp::verify() { return Verify(*this); }

//===----------------------------------------------------------------------===//
// ConcatOffsetOp

#include "mlir/IR/Operation.h"  // from @llvm-project
#include "mlir/IR/Types.h"  // from @llvm-project
#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFL {

namespace {