class PrepareDynamicRangeQuantizableOp
    : public OpRewritePattern<arith::ConstantOp> {
 public:
  explicit PrepareDynamicRangeQuantizableOp(
      MLIRContext* context, const quant::QuantizationSpecs& quant_specs,
      llvm::SetVector<Operation*>* const visited_nonquantizable_ops)
      : OpRewritePattern<arith::ConstantOp>(context),
        visited_nonquantizable_ops_(visited_nonquantizable_ops),

limitations under the License.
==============================================================================*/

#include <memory>
#include <utility>

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/Pass/PassRegistry.h"  // from @llvm-project

// CHECK-DAG:       [[VAL_8:%.*]] = arith.constant dense<0.000000e+00> : tensor<16x16xf32>
// CHECK-DAG:       [[VAL_9:%.*]] = arith.constant dense<0.000000e+00> : tensor<16x8xf32>
// CHECK-DAG:       [[VAL_10:%.*]] = arith.constant dense<0.000000e+00> : tensor<16xf32>
// CHECK-DAG:       [[VAL_11:%.*]] = arith.constant dense<0.000000e+00> : tensor<1x16xf32>
// CHECK-DAG:       [[VAL_12:%.*]] = arith.constant dense<0.000000e+00> : tensor<8x16xf32>

    }
  });
}

arith::ConstantOp QuantizationDriver::DuplicateConstantOpIfNeeded(
    arith::ConstantOp op, Operation* target_op, const int operand_index) {
  if (op.getResult().hasOneUse()) {
    return op;
  }
  OpBuilder builder(op->getContext());
  builder.setInsertionPointAfter(op);
  arith::ConstantOp new_op = cast<arith::ConstantOp>(builder.clone(*op));

limitations under the License.
==============================================================================*/

#include "llvm/Support/raw_ostream.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Tools/mlir-opt/MlirOptMain.h"  // from @llvm-project

  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

  %q_input= "quantfork.qcast"(%input) : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.58810077742034317:-128>>

  // CHECK-LABEL: init_all_tables
  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

#define TENSORFLOW_COMPILER_MLIR_LITE_EXPERIMENTAL_TAC_COMMON_UTILS_H_

#include "llvm/Support/Casting.h"
#include "mlir/Bytecode/BytecodeOpInterface.h"  // from @llvm-project
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantTypes.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project

  %size_splits_0 = arith.constant dense<[8, 8]> : tensor<2xi32>
  %split_dim_0 = arith.constant dense<0> : tensor<i32>
  %0, %1 = "tfl.split_v"(%arg0, %size_splits_0, %split_dim_0) {num_splits = 2 : i32} : (tensor<16x4xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<8x4xf32>, tensor<8x4xf32>)

  %size_splits_1 = arith.constant dense<[2, 2]> : tensor<2xi32>
  %split_dim_1 = arith.constant dense<1> : tensor<i32>

#include <gtest/gtest.h>
#include "flatbuffers/buffer.h"  // from @flatbuffers
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "flatbuffers/string.h"  // from @flatbuffers
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/DialectRegistry.h"  // from @llvm-project