}

 private:
  // Return true is there are still unfilled ODS slots for adding more inputs.
  bool IsNextODSArgAvailable();

  MLIRContext* context_;
  MlirFunctionContext* function_context_;
  SmallVector<Value, 8> operands_;
  llvm::StringMap<Attribute> attrs_;
  std::unique_ptr<OperationState> state_;
  // This is the index of the next ODS operand that will be added with AddInput
  // or AddInput;

#include <functional>
#include <iterator>
#include <memory>
#include <optional>
#include <string>
#include <utility>

#include "absl/memory/memory.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/Support/Casting.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/MathExtras.h"
#include "llvm/Support/raw_ostream.h"

#include <optional>
#include <string>

#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Support/Casting.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/raw_ostream.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

#include "absl/log/log.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/string_view.h"
#include "absl/types/span.h"
#include "llvm/ADT/SmallVector.h"
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/OwningOpRef.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project

#include "flatbuffers/string.h"  // from @flatbuffers
#include "flatbuffers/vector.h"  // from @flatbuffers
#include "llvm/ADT/APFloat.h"
#include "llvm/ADT/APInt.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/ADT/StringSwitch.h"
#include "llvm/ADT/Twine.h"
#include "llvm/Support/ErrorHandling.h"
#include "mlir/IR/AttrTypeSubElements.h"  // from @llvm-project

  EXPECT_TRUE(failed(CastI64ToI32(llvm::maxIntN(32) + 10)));
}

TEST_F(AttrsAndConstraintsTest, I64ArrayInI32RangeAreCastedCorrectly) {
  const SmallVector<int64_t> array_i64 = {llvm::minIntN(32), -2, -1, 0, 1, 2,
                                          llvm::maxIntN(32)};

  FailureOr<SmallVector<int32_t>> array_i32 = CastI64ArrayToI32(array_i64);
  EXPECT_TRUE(succeeded(array_i32));
  EXPECT_THAT(
      *array_i32,

    expand_op.setOperand(0, stb_op.getInput());
    // Calculate the shape for expand.
    auto input_shape =
        mlir::cast<ShapedType>(stb_op.getInput().getType()).getShape();
    SmallVector<int64_t, 4> expand_shape(input_shape.begin(),
                                         input_shape.end());
    expand_shape.insert(expand_shape.begin() + expand_axis, 1);

    auto transpose_op = cast<TransposeOp>(op.getOperand(0).getDefiningOp());

    const auto result_type = mlir::cast<TensorType>(op.getResult(0).getType());
    const SmallVector<int64_t> new_result_shape =
        Permute<int64_t>(result_type.getShape(), kNchwToNhwcPermutation);

    const TensorType new_result_type =
        result_type.cloneWith(new_result_shape, result_type.getElementType());

  ArrayRef<Type> inputs = quantization_func.getFunctionType().getInputs();
  Block* block = rewriter.createBlock(
      &quantization_func.getBody(), quantization_func.begin(), inputs,
      SmallVector<Location>(inputs.size(), quantization_func.getLoc()));
  func_input_arg = block->getArgument(0);
  return quantization_func;
}

// Post-actions after adding quantization logics. Post-actions include

    // Create the TF op
    OperationState op_state(op.getLoc(), tf_op_full_name);
    op_state.addOperands(op.getOperands());
    op_state.addTypes(op.getResultTypes());

    SmallVector<NamedAttribute, 2> attrs;
    std::string parsed_op_name;
    tensorflow::NodeDef node_def;
    if (failed(ParseCustomOption(op.getCustomOption().getValue(), op.getLoc(),