#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/core/transforms/toposort/pass.h"
#include "tensorflow/core/util/device_name_utils.h"

namespace mlir {
namespace TF {
namespace {

// FIXME: This should be consistent with
// tensorflow::kImportModelDefaultGraphFuncName

      entry->compilation_status = *compilation_status;
    }
    if (compilation_result.has_value()) {
      entry->compilation_result = std::move(*compilation_result);
    }
    if (executable.has_value()) {
      entry->executable = std::move(*executable);
    }
  }

  VLOG(4) << "Added/updated cache entry: key=" << key.HumanString()
          << ", entry=" << entry->DebugString();
}

  TF_DerivedResultTypeListAttr Tout = TF_DerivedResultTypeListAttr<0>;

  let hasVerifier = 1;
}

// TODO(chky): Consider adding this op to tensorflow core ops.
def TF_PwStreamResultsOp : TF_Op<"PwStreamResults"> {
  let summary = "Streams results back to the controller";

  let description = [{
    This op is a TensorFlow op that represents "streamed outputs", where

#include "tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_collector_min_max.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace calibrator {
namespace {

using ::testing::ElementsAre;

TEST(CalibrationStatisticsCollectorTest, SimpleMinMax) {

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

#include <algorithm>
#include <memory>
#include <utility>
#include <vector>

#include "Eigen/Core"  // from @eigen_archive
#include "llvm/ADT/SetVector.h"
#include "llvm/Support/Debug.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/core/platform/test.h"

namespace mlir {
namespace TFL {

func::FuncOp createLstmCompositeFunc(mlir::Builder* builder, bool ln,
                                     bool cifg) {
  SmallVector<int64_t, 2> input_shape{1, 2};

      // as its result index.
      if (is_func && isa<func::ReturnOp>(user)) {
        assert(!info.IsResultIndexAssigned() &&
               "Expect resource argument to returned no more than once");
        info.result_index = use.getOperandNumber();
        continue;
      }

      auto read = dyn_cast<TF::ReadVariableOp>(user);
      auto write = dyn_cast<TF::AssignVariableOp>(user);

#include "tensorflow/compiler/tf2xla/cc/ops/xla_ops.h"
#include "xla/status_macros.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/graph/algorithm.h"
#include "tensorflow/core/public/session_options.h"
#include "tensorflow/core/util/device_name_utils.h"
#include "tensorflow/core/util/dump_graph.h"

namespace tensorflow {
namespace {

// StatusOrOptional<T> instances hold
//

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/core/util/matmul_bcast.h"

namespace mlir {
namespace TF {

namespace {

template <typename BatchMatMulOpType>
class ConvertTFBatchMatMulOp : public OpRewritePattern<BatchMatMulOpType> {

// This test runs twice:
//   1. Through FILECHECK_OPTS="" FileCheck with chlo legalization disabled since verifying
//      that the chlo ops emit produces more useful tests.
//   2. With chlo legalization enabled, verifying diagnostics to pick up any
//      issues with the full lowering (can catch some broadcasting corner
//      cases which emit with a warning).